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Graphics\u003c/h1\u003e\u003ca id=\"user-content-tensorflow-graphics\" class=\"anchor\" aria-label=\"Permalink: TensorFlow Graphics\" href=\"#tensorflow-graphics\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://opensource.org/licenses/Apache-2.0\" 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data-canonical-src=\"https://img.shields.io/pypi/pyversions/tensorflow-graphics.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://pypi.org/project/tensorflow-graphics/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/bf4b58af2f06644ea8308fa6b558b6116b92c81ba0cb9c70c7e15f64c071f255/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f74656e736f72666c6f772d67726170686963732e737667\" alt=\"PyPI release version\" data-canonical-src=\"https://img.shields.io/pypi/v/tensorflow-graphics.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://pepy.tech/project/tensorflow-graphics\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/358bd7f550a9b70e30d0e8778884fb5cb0e26dfdda3eac5f21956c18efcd1208/68747470733a2f2f706570792e746563682f62616467652f74656e736f72666c6f772d6772617068696373\" alt=\"Downloads\" data-canonical-src=\"https://pepy.tech/badge/tensorflow-graphics\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe last few years have seen a rise in novel differentiable graphics layers\nwhich can be inserted in neural network architectures. From spatial transformers\nto differentiable graphics renderers, these new layers leverage the knowledge\nacquired over years of computer vision and graphics research to build new and\nmore efficient network architectures. Explicitly modeling geometric priors and\nconstraints into neural networks opens up the door to architectures that can be\ntrained robustly, efficiently, and more importantly, in a self-supervised\nfashion.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAt a high level, a computer graphics pipeline requires a representation of 3D\nobjects and their absolute positioning in the scene, a description of the\nmaterial they are made of, lights and a camera. This scene description is then\ninterpreted by a renderer to generate a synthetic rendering.\u003c/p\u003e\n\u003cdiv align=\"center\" dir=\"auto\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/2f328205f2cfe9251bf74cef79b05f5fd814a217b578c33a658ceb1743cf24f6/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f74656e736f72666c6f772d67726170686963732f6769742f726561646d652f67726170686963732e6a7067\"\u003e\u003cimg border=\"0\" src=\"https://camo.githubusercontent.com/2f328205f2cfe9251bf74cef79b05f5fd814a217b578c33a658ceb1743cf24f6/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f74656e736f72666c6f772d67726170686963732f6769742f726561646d652f67726170686963732e6a7067\" width=\"600\" data-canonical-src=\"https://storage.googleapis.com/tensorflow-graphics/git/readme/graphics.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIn comparison, a computer vision system would start from an image and try to\ninfer the parameters of the scene. This allows the prediction of which objects\nare in the scene, what materials they are made of, and their three-dimensional\nposition and orientation.\u003c/p\u003e\n\u003cdiv align=\"center\" dir=\"auto\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/e6c4bb871bf095e26a1eece34606191bd266a7eea4c2be09b6c07fcf4b726b57/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f74656e736f72666c6f772d67726170686963732f6769742f726561646d652f63762e6a7067\"\u003e\u003cimg border=\"0\" src=\"https://camo.githubusercontent.com/e6c4bb871bf095e26a1eece34606191bd266a7eea4c2be09b6c07fcf4b726b57/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f74656e736f72666c6f772d67726170686963732f6769742f726561646d652f63762e6a7067\" width=\"600\" data-canonical-src=\"https://storage.googleapis.com/tensorflow-graphics/git/readme/cv.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTraining machine learning systems capable of solving these complex 3D vision\ntasks most often requires large quantities of data. As labelling data is a\ncostly and complex process, it is important to have mechanisms to design machine\nlearning models that can comprehend the three dimensional world while being\ntrained without much supervision. Combining computer vision and computer\ngraphics techniques provides a unique opportunity to leverage the vast amounts\nof readily available unlabelled data. As illustrated in the image below, this\ncan, for instance, be achieved using analysis by synthesis where the vision\nsystem extracts the scene parameters and the graphics system renders back an\nimage based on them. If the rendering matches the original image, the vision\nsystem has accurately extracted the scene parameters. In this setup, computer\nvision and computer graphics go hand in hand, forming a single machine learning\nsystem similar to an autoencoder, which can be trained in a self-supervised\nmanner.\u003c/p\u003e\n\u003cdiv align=\"center\" dir=\"auto\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/2a420c55d73043ae03c4e49dd4bdcee8009b2a67b4deaa4323a38d23d91f540d/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f74656e736f72666c6f772d67726170686963732f6769742f726561646d652f63765f67726170686963732e6a7067\"\u003e\u003cimg border=\"0\" src=\"https://camo.githubusercontent.com/2a420c55d73043ae03c4e49dd4bdcee8009b2a67b4deaa4323a38d23d91f540d/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f74656e736f72666c6f772d67726170686963732f6769742f726561646d652f63765f67726170686963732e6a7067\" width=\"600\" data-canonical-src=\"https://storage.googleapis.com/tensorflow-graphics/git/readme/cv_graphics.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTensorflow Graphics is being developed to help tackle these types of challenges\nand to do so, it provides a set of differentiable graphics and geometry layers\n(e.g. cameras, reflectance models, spatial transformations, mesh convolutions)\nand 3D viewer functionalities (e.g. 3D TensorBoard) that can be used to train\nand debug your machine learning models of choice.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstalling TensorFlow Graphics\u003c/h2\u003e\u003ca id=\"user-content-installing-tensorflow-graphics\" class=\"anchor\" aria-label=\"Permalink: Installing TensorFlow Graphics\" href=\"#installing-tensorflow-graphics\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eSee the \u003ca href=\"https://github.com/tensorflow/graphics/blob/master/tensorflow_graphics/g3doc/install.md\"\u003einstall\u003c/a\u003e\ndocumentation for instructions on how to install TensorFlow Graphics.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAPI Documentation\u003c/h2\u003e\u003ca id=\"user-content-api-documentation\" class=\"anchor\" aria-label=\"Permalink: API Documentation\" href=\"#api-documentation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou can find the API documentation\n\u003ca href=\"https://github.com/tensorflow/graphics/blob/master/tensorflow_graphics/g3doc/api_docs/python/tfg.md\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCompatibility\u003c/h2\u003e\u003ca id=\"user-content-compatibility\" class=\"anchor\" aria-label=\"Permalink: Compatibility\" href=\"#compatibility\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTensorFlow Graphics is fully compatible with the latest stable release of\nTensorFlow, tf-nightly, and tf-nightly-2.0-preview. All the functions are\ncompatible with graph and eager execution.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDebugging\u003c/h2\u003e\u003ca id=\"user-content-debugging\" class=\"anchor\" aria-label=\"Permalink: Debugging\" href=\"#debugging\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTensorflow Graphics heavily relies on L2 normalized tensors, as well as having\nthe inputs to specific function be in a pre-defined range. Checking for all of\nthis takes cycles, and hence is not activated by default. It is recommended to\nturn these checks on during a couple epochs of training to make sure that\neverything behaves as expected. This\n\u003ca href=\"https://github.com/tensorflow/graphics/blob/master/tensorflow_graphics/g3doc/debug_mode.md\"\u003epage\u003c/a\u003e\nprovides the instructions to enable these checks.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eColab tutorials\u003c/h2\u003e\u003ca id=\"user-content-colab-tutorials\" class=\"anchor\" aria-label=\"Permalink: Colab tutorials\" href=\"#colab-tutorials\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo help you get started with some of the functionalities provided by TF\nGraphics, some Colab notebooks are available below and roughly ordered by\ndifficulty. These Colabs touch upon a large range of topics including, object\npose estimation, interpolation, object materials, lighting, non-rigid surface\ndeformation, spherical harmonics, and mesh convolutions.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eNOTE: the tutorials are maintained carefully. However, they are not considered\npart of the API and they can change at any time without warning. It is not\nadvised to write code that takes dependency on them.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBeginner\u003c/h3\u003e\u003ca id=\"user-content-beginner\" class=\"anchor\" aria-label=\"Permalink: Beginner\" href=\"#beginner\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv align=\"center\" dir=\"auto\"\u003e\n \u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n \u003ctbody\u003e\u003ctr\u003e\n \u003cth\u003e\u003ca href=\"https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/6dof_alignment.ipynb\" rel=\"nofollow\"\u003eObject pose estimation\u003c/a\u003e\u003c/th\u003e\n \u003cth\u003e\u003ca href=\"https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/intrinsics_optimization.ipynb\" rel=\"nofollow\"\u003eCamera intrinsics optimization\u003c/a\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"center\"\u003e\n \u003ca href=\"https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/6dof_alignment.ipynb\" rel=\"nofollow\"\u003e\u003cimg border=\"0\" src=\"https://camo.githubusercontent.com/84250e22660a1790d0d468c58223b5f05541f9a58a80df0f9c770aa886ea3bee/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f74656e736f72666c6f772d67726170686963732f6e6f7465626f6f6b732f36646f665f706f73652f7468756d626e61696c2e6a7067\" width=\"200\" height=\"200\" data-canonical-src=\"https://storage.googleapis.com/tensorflow-graphics/notebooks/6dof_pose/thumbnail.jpg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003ctd align=\"center\"\u003e\n \u003ca href=\"https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/intrinsics_optimization.ipynb\" rel=\"nofollow\"\u003e\u003cimg border=\"0\" src=\"https://camo.githubusercontent.com/7c5f68de1a0e7a8c2cfc6d69c798b05df4bb1ef4a03cf75a146458803b2f4e16/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f74656e736f72666c6f772d67726170686963732f6e6f7465626f6f6b732f696e7472696e736963732f696e7472696e736963735f7468756d626e61696c2e706e67\" width=\"200\" height=\"200\" data-canonical-src=\"https://storage.googleapis.com/tensorflow-graphics/notebooks/intrinsics/intrinsics_thumbnail.png\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eIntermediate\u003c/h3\u003e\u003ca id=\"user-content-intermediate\" class=\"anchor\" aria-label=\"Permalink: Intermediate\" href=\"#intermediate\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv align=\"center\" dir=\"auto\"\u003e\n \u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n \u003ctbody\u003e\u003ctr\u003e\n \u003cth\u003e\u003ca href=\"https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/interpolation.ipynb\" rel=\"nofollow\"\u003eB-spline and slerp interpolation\u003c/a\u003e\u003c/th\u003e\n \u003cth\u003e\u003ca href=\"https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/reflectance.ipynb\" rel=\"nofollow\"\u003eReflectance\u003c/a\u003e\u003c/th\u003e\n \u003cth\u003e\u003ca href=\"https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/non_rigid_deformation.ipynb\" rel=\"nofollow\"\u003eNon-rigid surface deformation\u003c/a\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/interpolation.ipynb\" rel=\"nofollow\"\u003e\u003cimg border=\"0\" src=\"https://camo.githubusercontent.com/38ab71a9045bb8458ed49226571f23f9f81c511378920a0612feae617820e3c2/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f74656e736f72666c6f772d67726170686963732f6e6f7465626f6f6b732f696e746572706f6c6174696f6e2f7468756d626e61696c2e706e67\" width=\"200\" height=\"200\" data-canonical-src=\"https://storage.googleapis.com/tensorflow-graphics/notebooks/interpolation/thumbnail.png\" style=\"max-width: 100%;\"\u003e \u003c/a\u003e\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/reflectance.ipynb\" rel=\"nofollow\"\u003e\u003cimg border=\"0\" src=\"https://camo.githubusercontent.com/0896a970e36b58f4f5462e18c94758437834d9a1f31b849728a33f215796c2b9/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f74656e736f72666c6f772d67726170686963732f6e6f7465626f6f6b732f7265666c656374616e63652f7468756d626e61696c2e706e67\" width=\"200\" height=\"200\" data-canonical-src=\"https://storage.googleapis.com/tensorflow-graphics/notebooks/reflectance/thumbnail.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/non_rigid_deformation.ipynb\" rel=\"nofollow\"\u003e\u003cimg border=\"0\" src=\"https://camo.githubusercontent.com/78fdbbc91d496376b46a09cca89677b13fa405fc7d20b16e172a0c5b012409fe/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f74656e736f72666c6f772d67726170686963732f6e6f7465626f6f6b732f6e6f6e5f72696769645f6465666f726d6174696f6e2f7468756d626e61696c2e6a7067\" width=\"200\" height=\"200\" data-canonical-src=\"https://storage.googleapis.com/tensorflow-graphics/notebooks/non_rigid_deformation/thumbnail.jpg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAdvanced\u003c/h3\u003e\u003ca id=\"user-content-advanced\" class=\"anchor\" aria-label=\"Permalink: Advanced\" href=\"#advanced\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv align=\"center\" dir=\"auto\"\u003e\n \u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n \u003ctbody\u003e\u003ctr\u003e\n \u003cth\u003e\u003ca href=\"https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/spherical_harmonics_approximation.ipynb\" rel=\"nofollow\"\u003eSpherical harmonics rendering\u003c/a\u003e\u003c/th\u003e\n \u003cth\u003e\u003ca href=\"https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/spherical_harmonics_optimization.ipynb\" rel=\"nofollow\"\u003eEnvironment map optimization\u003c/a\u003e\u003c/th\u003e\n \u003cth\u003e\u003ca href=\"https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/mesh_segmentation_demo.ipynb\" rel=\"nofollow\"\u003eSemantic mesh segmentation\u003c/a\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/spherical_harmonics_approximation.ipynb\" rel=\"nofollow\"\u003e\u003cimg border=\"0\" src=\"https://camo.githubusercontent.com/dfb6d65d88a054619c96db2fc0cfdbbde413bea334f9c1219359db25cd11e494/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f74656e736f72666c6f772d67726170686963732f6e6f7465626f6f6b732f73685f72656e646572696e672f7468756d626e61696c2e706e67\" width=\"200\" height=\"200\" data-canonical-src=\"https://storage.googleapis.com/tensorflow-graphics/notebooks/sh_rendering/thumbnail.png\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/spherical_harmonics_optimization.ipynb\" rel=\"nofollow\"\u003e\u003cimg border=\"0\" src=\"https://camo.githubusercontent.com/1a601cad85559579cc351fe356d5d98ac32174250cddc7086a55f5d20cfd9823/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f74656e736f72666c6f772d67726170686963732f6e6f7465626f6f6b732f656e7669726f6e6d656e745f6c69676874696e672f7468756d626e61696c2e706e67\" width=\"200\" height=\"200\" data-canonical-src=\"https://storage.googleapis.com/tensorflow-graphics/notebooks/environment_lighting/thumbnail.png\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\u003c/td\u003e\n \u003ctd align=\"center\"\u003e\u003ca href=\"https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/mesh_segmentation_demo.ipynb\" rel=\"nofollow\"\u003e\u003cimg border=\"0\" src=\"https://camo.githubusercontent.com/fc840d0515043e6259d6871e0ad0303eab66a76c32229d6f9c2925a0a522f070/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f74656e736f72666c6f772d67726170686963732f6e6f7465626f6f6b732f6d6573685f7365676d656e746174696f6e2f7468756d626e61696c2e6a7067\" width=\"200\" height=\"200\" data-canonical-src=\"https://storage.googleapis.com/tensorflow-graphics/notebooks/mesh_segmentation/thumbnail.jpg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTensorBoard 3D\u003c/h2\u003e\u003ca id=\"user-content-tensorboard-3d\" class=\"anchor\" aria-label=\"Permalink: TensorBoard 3D\" href=\"#tensorboard-3d\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eVisual debugging is a great way to assess whether an experiment is going in the\nright direction. To this end, TensorFlow Graphics comes with a TensorBoard\nplugin to interactively visualize 3D meshes and point clouds.\n\u003ca href=\"https://colab.research.google.com/github/tensorflow/tensorboard/blob/master/tensorboard/plugins/mesh/Mesh_Plugin_Tensorboard.ipynb\" rel=\"nofollow\"\u003eThis demo\u003c/a\u003e\nshows how to use the plugin. Follow\n\u003ca href=\"https://github.com/tensorflow/graphics/blob/master/tensorflow_graphics/g3doc/tensorboard.md\"\u003ethese instructions\u003c/a\u003e\nto install and configure TensorBoard 3D. Note that TensorBoard 3D is currently\nnot compatible with eager execution nor TensorFlow 2.\u003c/p\u003e\n\u003cdiv align=\"center\" dir=\"auto\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/4e958a01aaa9d6f016b37533b911b9040b01a512393be64c5fadab21b76579a0/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f74656e736f72666c6f772d67726170686963732f6769742f726561646d652f74656e736f72626f6172645f706c7567696e2e6a7067\"\u003e\u003cimg border=\"0\" src=\"https://camo.githubusercontent.com/4e958a01aaa9d6f016b37533b911b9040b01a512393be64c5fadab21b76579a0/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f74656e736f72666c6f772d67726170686963732f6769742f726561646d652f74656e736f72626f6172645f706c7567696e2e6a7067\" width=\"1280\" data-canonical-src=\"https://storage.googleapis.com/tensorflow-graphics/git/readme/tensorboard_plugin.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eComing next...\u003c/h2\u003e\u003ca id=\"user-content-coming-next\" class=\"anchor\" aria-label=\"Permalink: Coming next...\" href=\"#coming-next\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAmong many things, we are hoping to release resamplers, additional 3D\nconvolution and pooling operators, and a differentiable rasterizer!\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFollow us on \u003ca href=\"https://twitter.com/_TFGraphics_\" rel=\"nofollow\"\u003eTwitter\u003c/a\u003e to hear about the\nlatest updates!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAdditional Information\u003c/h2\u003e\u003ca id=\"user-content-additional-information\" class=\"anchor\" aria-label=\"Permalink: Additional Information\" href=\"#additional-information\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou may use this software under the\n\u003ca href=\"https://github.com/tensorflow/graphics/blob/master/LICENSE\"\u003eApache 2.0 License\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCommunity\u003c/h2\u003e\u003ca id=\"user-content-community\" class=\"anchor\" aria-label=\"Permalink: Community\" href=\"#community\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAs part of TensorFlow, we're committed to fostering an open and welcoming\nenvironment.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://stackoverflow.com/questions/tagged/tensorflow\" rel=\"nofollow\"\u003eStack Overflow\u003c/a\u003e: Ask\nor answer technical questions.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/tensorflow/graphics/issues\"\u003eGitHub\u003c/a\u003e: Report bugs or make\nfeature requests.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://blog.tensorflow.org/\" rel=\"nofollow\"\u003eTensorFlow Blog\u003c/a\u003e: Stay up to date on content\nfrom the TensorFlow team and best articles from the community.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://youtube.com/tensorflow/\" rel=\"nofollow\"\u003eYoutube Channel\u003c/a\u003e: Follow TensorFlow shows.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eReferences\u003c/h2\u003e\u003ca id=\"user-content-references\" class=\"anchor\" aria-label=\"Permalink: References\" href=\"#references\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you use TensorFlow Graphics in your research, please reference it as:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@inproceedings{TensorflowGraphicsIO2019,\n author = {Oztireli, Cengiz and Valentin, Julien and Keskin, Cem and Pidlypenskyi, Pavel and Makadia, Ameesh and Sud, Avneesh and Bouaziz, Sofien},\n title = {TensorFlow Graphics: Computer Graphics Meets Deep Learning},\n year = {2019}\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@inproceedings{TensorflowGraphicsIO2019,\n author = {Oztireli, Cengiz and Valentin, Julien and Keskin, Cem and Pidlypenskyi, Pavel and Makadia, Ameesh and Sud, Avneesh and Bouaziz, Sofien},\n title = {TensorFlow Graphics: Computer Graphics Meets Deep Learning},\n year = {2019}\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eContact\u003c/h3\u003e\u003ca id=\"user-content-contact\" class=\"anchor\" aria-label=\"Permalink: Contact\" href=\"#contact\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWant to reach out? E-mail us at \u003ca href=\"mailto:tf-graphics-contact@google.com\"\u003etf-graphics-contact@google.com\u003c/a\u003e!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eContributors - in alphabetical order\u003c/h3\u003e\u003ca id=\"user-content-contributors---in-alphabetical-order\" class=\"anchor\" aria-label=\"Permalink: Contributors - in alphabetical order\" href=\"#contributors---in-alphabetical-order\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSofien Bouaziz (\u003ca href=\"mailto:sofien@google.com\"\u003esofien@google.com\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eJay Busch\u003c/li\u003e\n\u003cli\u003eForrester Cole\u003c/li\u003e\n\u003cli\u003eAmbrus Csaszar\u003c/li\u003e\n\u003cli\u003eBoyang Deng\u003c/li\u003e\n\u003cli\u003eAriel Gordon\u003c/li\u003e\n\u003cli\u003eChristian Häne\u003c/li\u003e\n\u003cli\u003eCem Keskin\u003c/li\u003e\n\u003cli\u003eAmeesh Makadia\u003c/li\u003e\n\u003cli\u003eCengiz Öztireli\u003c/li\u003e\n\u003cli\u003eRohit Pandey\u003c/li\u003e\n\u003cli\u003eRomain Prévost\u003c/li\u003e\n\u003cli\u003ePavel Pidlypenskyi\u003c/li\u003e\n\u003cli\u003eStefan Popov\u003c/li\u003e\n\u003cli\u003eKonstantinos Rematas\u003c/li\u003e\n\u003cli\u003eOmar Sanseviero\u003c/li\u003e\n\u003cli\u003eAviv Segal\u003c/li\u003e\n\u003cli\u003eAvneesh Sud\u003c/li\u003e\n\u003cli\u003eAndrea Tagliasacchi\u003c/li\u003e\n\u003cli\u003eAnastasia Tkach\u003c/li\u003e\n\u003cli\u003eJulien Valentin\u003c/li\u003e\n\u003cli\u003eHe Wang\u003c/li\u003e\n\u003cli\u003eYinda Zhang\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"TensorFlow Graphics","anchor":"tensorflow-graphics","htmlText":"TensorFlow Graphics"},{"level":2,"text":"Overview","anchor":"overview","htmlText":"Overview"},{"level":2,"text":"Installing TensorFlow Graphics","anchor":"installing-tensorflow-graphics","htmlText":"Installing TensorFlow Graphics"},{"level":2,"text":"API Documentation","anchor":"api-documentation","htmlText":"API Documentation"},{"level":2,"text":"Compatibility","anchor":"compatibility","htmlText":"Compatibility"},{"level":2,"text":"Debugging","anchor":"debugging","htmlText":"Debugging"},{"level":2,"text":"Colab tutorials","anchor":"colab-tutorials","htmlText":"Colab tutorials"},{"level":3,"text":"Beginner","anchor":"beginner","htmlText":"Beginner"},{"level":3,"text":"Intermediate","anchor":"intermediate","htmlText":"Intermediate"},{"level":3,"text":"Advanced","anchor":"advanced","htmlText":"Advanced"},{"level":2,"text":"TensorBoard 3D","anchor":"tensorboard-3d","htmlText":"TensorBoard 3D"},{"level":2,"text":"Coming next...","anchor":"coming-next","htmlText":"Coming next..."},{"level":2,"text":"Additional Information","anchor":"additional-information","htmlText":"Additional 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order"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Ftensorflow%2Fgraphics"}},{"displayName":"LICENSE","repoName":"graphics","refName":"master","path":"LICENSE","preferredFileType":"license","tabName":"Apache-2.0","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Ftensorflow%2Fgraphics"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-1583894afd38.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-67668e8c2caa.js","githubDevUrl":null,"enabled_features":{"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"copilot_conversational_ux_embedding_update":false,"copilot_smell_icebreaker_ux":true,"copilot_workspace":false,"blob_edit_unsaved_changes_storage":true,"accessible_code_button":true}}}}</script> <div 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TensorFlow Graphics" href="#tensorflow-graphics"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a href="https://opensource.org/licenses/Apache-2.0" rel="nofollow"><img src="https://camo.githubusercontent.com/859a1a0bc85ce8bbd7a730a274fec5c9e77c4726ffdf6aa762a78685e26033a4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d417061636865253230322e302d626c75652e737667" alt="License" data-canonical-src="https://img.shields.io/badge/License-Apache%202.0-blue.svg" style="max-width: 100%;"></a> <a href="https://github.com/tensorflow/graphics/actions"><img src="https://github.com/tensorflow/graphics/workflows/Build/badge.svg?branch=master" alt="Build" style="max-width: 100%;"></a> <a href="https://coveralls.io/github/tensorflow/graphics" rel="nofollow"><img src="https://camo.githubusercontent.com/e52b8e40a1f9c1061b04d680ce6fda6ffaf3c2d6e7a8c41026658653ded087c7/68747470733a2f2f696d672e736869656c64732e696f2f636f766572616c6c732f6769746875622f74656e736f72666c6f772f67726170686963732e737667" alt="Code coverage" data-canonical-src="https://img.shields.io/coveralls/github/tensorflow/graphics.svg" style="max-width: 100%;"></a> <a href="https://pypi.org/project/tensorflow-graphics/" rel="nofollow"><img src="https://camo.githubusercontent.com/c8b1605eb9237a42b4f33997ea2c3b29955506124ea3cef63f26092fd36dabc7/68747470733a2f2f696d672e736869656c64732e696f2f707970692f7374617475732f74656e736f72666c6f772d67726170686963732e737667" alt="PyPI project status" data-canonical-src="https://img.shields.io/pypi/status/tensorflow-graphics.svg" style="max-width: 100%;"></a> <a href="https://pypi.org/project/tensorflow-graphics/" rel="nofollow"><img src="https://camo.githubusercontent.com/80cb1fc522e04a31083f6684ab77719cae311bcd31d12f5a34d8f3ef38bd35c4/68747470733a2f2f696d672e736869656c64732e696f2f707970692f707976657273696f6e732f74656e736f72666c6f772d67726170686963732e737667" alt="Supported Python version" data-canonical-src="https://img.shields.io/pypi/pyversions/tensorflow-graphics.svg" style="max-width: 100%;"></a> <a href="https://pypi.org/project/tensorflow-graphics/" rel="nofollow"><img src="https://camo.githubusercontent.com/bf4b58af2f06644ea8308fa6b558b6116b92c81ba0cb9c70c7e15f64c071f255/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f74656e736f72666c6f772d67726170686963732e737667" alt="PyPI release version" data-canonical-src="https://img.shields.io/pypi/v/tensorflow-graphics.svg" style="max-width: 100%;"></a> <a href="https://pepy.tech/project/tensorflow-graphics" rel="nofollow"><img src="https://camo.githubusercontent.com/358bd7f550a9b70e30d0e8778884fb5cb0e26dfdda3eac5f21956c18efcd1208/68747470733a2f2f706570792e746563682f62616467652f74656e736f72666c6f772d6772617068696373" alt="Downloads" data-canonical-src="https://pepy.tech/badge/tensorflow-graphics" style="max-width: 100%;"></a></p> <p dir="auto">The last few years have seen a rise in novel differentiable graphics layers which can be inserted in neural network architectures. From spatial transformers to differentiable graphics renderers, these new layers leverage the knowledge acquired over years of computer vision and graphics research to build new and more efficient network architectures. Explicitly modeling geometric priors and constraints into neural networks opens up the door to architectures that can be trained robustly, efficiently, and more importantly, in a self-supervised fashion.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Overview</h2><a id="user-content-overview" class="anchor" aria-label="Permalink: Overview" href="#overview"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">At a high level, a computer graphics pipeline requires a representation of 3D objects and their absolute positioning in the scene, a description of the material they are made of, lights and a camera. This scene description is then interpreted by a renderer to generate a synthetic rendering.</p> <div align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://camo.githubusercontent.com/2f328205f2cfe9251bf74cef79b05f5fd814a217b578c33a658ceb1743cf24f6/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f74656e736f72666c6f772d67726170686963732f6769742f726561646d652f67726170686963732e6a7067"><img border="0" src="https://camo.githubusercontent.com/2f328205f2cfe9251bf74cef79b05f5fd814a217b578c33a658ceb1743cf24f6/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f74656e736f72666c6f772d67726170686963732f6769742f726561646d652f67726170686963732e6a7067" width="600" data-canonical-src="https://storage.googleapis.com/tensorflow-graphics/git/readme/graphics.jpg" style="max-width: 100%;"></a> </div> <p dir="auto">In comparison, a computer vision system would start from an image and try to infer the parameters of the scene. This allows the prediction of which objects are in the scene, what materials they are made of, and their three-dimensional position and orientation.</p> <div align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://camo.githubusercontent.com/e6c4bb871bf095e26a1eece34606191bd266a7eea4c2be09b6c07fcf4b726b57/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f74656e736f72666c6f772d67726170686963732f6769742f726561646d652f63762e6a7067"><img border="0" src="https://camo.githubusercontent.com/e6c4bb871bf095e26a1eece34606191bd266a7eea4c2be09b6c07fcf4b726b57/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f74656e736f72666c6f772d67726170686963732f6769742f726561646d652f63762e6a7067" width="600" data-canonical-src="https://storage.googleapis.com/tensorflow-graphics/git/readme/cv.jpg" style="max-width: 100%;"></a> </div> <p dir="auto">Training machine learning systems capable of solving these complex 3D vision tasks most often requires large quantities of data. As labelling data is a costly and complex process, it is important to have mechanisms to design machine learning models that can comprehend the three dimensional world while being trained without much supervision. Combining computer vision and computer graphics techniques provides a unique opportunity to leverage the vast amounts of readily available unlabelled data. As illustrated in the image below, this can, for instance, be achieved using analysis by synthesis where the vision system extracts the scene parameters and the graphics system renders back an image based on them. If the rendering matches the original image, the vision system has accurately extracted the scene parameters. In this setup, computer vision and computer graphics go hand in hand, forming a single machine learning system similar to an autoencoder, which can be trained in a self-supervised manner.</p> <div align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://camo.githubusercontent.com/2a420c55d73043ae03c4e49dd4bdcee8009b2a67b4deaa4323a38d23d91f540d/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f74656e736f72666c6f772d67726170686963732f6769742f726561646d652f63765f67726170686963732e6a7067"><img border="0" src="https://camo.githubusercontent.com/2a420c55d73043ae03c4e49dd4bdcee8009b2a67b4deaa4323a38d23d91f540d/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f74656e736f72666c6f772d67726170686963732f6769742f726561646d652f63765f67726170686963732e6a7067" width="600" data-canonical-src="https://storage.googleapis.com/tensorflow-graphics/git/readme/cv_graphics.jpg" style="max-width: 100%;"></a> </div> <p dir="auto">Tensorflow Graphics is being developed to help tackle these types of challenges and to do so, it provides a set of differentiable graphics and geometry layers (e.g. cameras, reflectance models, spatial transformations, mesh convolutions) and 3D viewer functionalities (e.g. 3D TensorBoard) that can be used to train and debug your machine learning models of choice.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Installing TensorFlow Graphics</h2><a id="user-content-installing-tensorflow-graphics" class="anchor" aria-label="Permalink: Installing TensorFlow Graphics" href="#installing-tensorflow-graphics"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">See the <a href="https://github.com/tensorflow/graphics/blob/master/tensorflow_graphics/g3doc/install.md">install</a> documentation for instructions on how to install TensorFlow Graphics.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">API Documentation</h2><a id="user-content-api-documentation" class="anchor" aria-label="Permalink: API Documentation" href="#api-documentation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">You can find the API documentation <a href="https://github.com/tensorflow/graphics/blob/master/tensorflow_graphics/g3doc/api_docs/python/tfg.md">here</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Compatibility</h2><a id="user-content-compatibility" class="anchor" aria-label="Permalink: Compatibility" href="#compatibility"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">TensorFlow Graphics is fully compatible with the latest stable release of TensorFlow, tf-nightly, and tf-nightly-2.0-preview. All the functions are compatible with graph and eager execution.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Debugging</h2><a id="user-content-debugging" class="anchor" aria-label="Permalink: Debugging" href="#debugging"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Tensorflow Graphics heavily relies on L2 normalized tensors, as well as having the inputs to specific function be in a pre-defined range. Checking for all of this takes cycles, and hence is not activated by default. It is recommended to turn these checks on during a couple epochs of training to make sure that everything behaves as expected. This <a href="https://github.com/tensorflow/graphics/blob/master/tensorflow_graphics/g3doc/debug_mode.md">page</a> provides the instructions to enable these checks.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Colab tutorials</h2><a id="user-content-colab-tutorials" class="anchor" aria-label="Permalink: Colab tutorials" href="#colab-tutorials"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">To help you get started with some of the functionalities provided by TF Graphics, some Colab notebooks are available below and roughly ordered by difficulty. These Colabs touch upon a large range of topics including, object pose estimation, interpolation, object materials, lighting, non-rigid surface deformation, spherical harmonics, and mesh convolutions.</p> <p dir="auto">NOTE: the tutorials are maintained carefully. However, they are not considered part of the API and they can change at any time without warning. It is not advised to write code that takes dependency on them.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Beginner</h3><a id="user-content-beginner" class="anchor" aria-label="Permalink: Beginner" href="#beginner"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div align="center" dir="auto"> <markdown-accessiblity-table><table> <tbody><tr> <th><a href="https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/6dof_alignment.ipynb" rel="nofollow">Object pose estimation</a></th> <th><a href="https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/intrinsics_optimization.ipynb" rel="nofollow">Camera intrinsics optimization</a></th> </tr> <tr> <td align="center"> <a href="https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/6dof_alignment.ipynb" rel="nofollow"><img border="0" src="https://camo.githubusercontent.com/84250e22660a1790d0d468c58223b5f05541f9a58a80df0f9c770aa886ea3bee/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f74656e736f72666c6f772d67726170686963732f6e6f7465626f6f6b732f36646f665f706f73652f7468756d626e61696c2e6a7067" width="200" height="200" data-canonical-src="https://storage.googleapis.com/tensorflow-graphics/notebooks/6dof_pose/thumbnail.jpg" style="max-width: 100%;"> </a> </td> <td align="center"> <a href="https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/intrinsics_optimization.ipynb" rel="nofollow"><img border="0" src="https://camo.githubusercontent.com/7c5f68de1a0e7a8c2cfc6d69c798b05df4bb1ef4a03cf75a146458803b2f4e16/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f74656e736f72666c6f772d67726170686963732f6e6f7465626f6f6b732f696e7472696e736963732f696e7472696e736963735f7468756d626e61696c2e706e67" width="200" height="200" data-canonical-src="https://storage.googleapis.com/tensorflow-graphics/notebooks/intrinsics/intrinsics_thumbnail.png" style="max-width: 100%;"> </a> </td> </tr> </tbody></table></markdown-accessiblity-table> </div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Intermediate</h3><a id="user-content-intermediate" class="anchor" aria-label="Permalink: Intermediate" href="#intermediate"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div align="center" dir="auto"> <markdown-accessiblity-table><table> <tbody><tr> <th><a href="https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/interpolation.ipynb" rel="nofollow">B-spline and slerp interpolation</a></th> <th><a href="https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/reflectance.ipynb" rel="nofollow">Reflectance</a></th> <th><a href="https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/non_rigid_deformation.ipynb" rel="nofollow">Non-rigid surface deformation</a></th> </tr> <tr> <td align="center"><a href="https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/interpolation.ipynb" rel="nofollow"><img border="0" src="https://camo.githubusercontent.com/38ab71a9045bb8458ed49226571f23f9f81c511378920a0612feae617820e3c2/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f74656e736f72666c6f772d67726170686963732f6e6f7465626f6f6b732f696e746572706f6c6174696f6e2f7468756d626e61696c2e706e67" width="200" height="200" data-canonical-src="https://storage.googleapis.com/tensorflow-graphics/notebooks/interpolation/thumbnail.png" style="max-width: 100%;"> </a></td> <td align="center"><a href="https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/reflectance.ipynb" rel="nofollow"><img border="0" src="https://camo.githubusercontent.com/0896a970e36b58f4f5462e18c94758437834d9a1f31b849728a33f215796c2b9/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f74656e736f72666c6f772d67726170686963732f6e6f7465626f6f6b732f7265666c656374616e63652f7468756d626e61696c2e706e67" width="200" height="200" data-canonical-src="https://storage.googleapis.com/tensorflow-graphics/notebooks/reflectance/thumbnail.png" style="max-width: 100%;"></a></td> <td align="center"><a href="https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/non_rigid_deformation.ipynb" rel="nofollow"><img border="0" src="https://camo.githubusercontent.com/78fdbbc91d496376b46a09cca89677b13fa405fc7d20b16e172a0c5b012409fe/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f74656e736f72666c6f772d67726170686963732f6e6f7465626f6f6b732f6e6f6e5f72696769645f6465666f726d6174696f6e2f7468756d626e61696c2e6a7067" width="200" height="200" data-canonical-src="https://storage.googleapis.com/tensorflow-graphics/notebooks/non_rigid_deformation/thumbnail.jpg" style="max-width: 100%;"> </a></td> </tr> </tbody></table></markdown-accessiblity-table> </div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Advanced</h3><a id="user-content-advanced" class="anchor" aria-label="Permalink: Advanced" href="#advanced"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div align="center" dir="auto"> <markdown-accessiblity-table><table> <tbody><tr> <th><a href="https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/spherical_harmonics_approximation.ipynb" rel="nofollow">Spherical harmonics rendering</a></th> <th><a href="https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/spherical_harmonics_optimization.ipynb" rel="nofollow">Environment map optimization</a></th> <th><a href="https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/mesh_segmentation_demo.ipynb" rel="nofollow">Semantic mesh segmentation</a></th> </tr> <tr> <td align="center"><a href="https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/spherical_harmonics_approximation.ipynb" rel="nofollow"><img border="0" src="https://camo.githubusercontent.com/dfb6d65d88a054619c96db2fc0cfdbbde413bea334f9c1219359db25cd11e494/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f74656e736f72666c6f772d67726170686963732f6e6f7465626f6f6b732f73685f72656e646572696e672f7468756d626e61696c2e706e67" width="200" height="200" data-canonical-src="https://storage.googleapis.com/tensorflow-graphics/notebooks/sh_rendering/thumbnail.png" style="max-width: 100%;"> </a></td> <td align="center"><a href="https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/spherical_harmonics_optimization.ipynb" rel="nofollow"><img border="0" src="https://camo.githubusercontent.com/1a601cad85559579cc351fe356d5d98ac32174250cddc7086a55f5d20cfd9823/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f74656e736f72666c6f772d67726170686963732f6e6f7465626f6f6b732f656e7669726f6e6d656e745f6c69676874696e672f7468756d626e61696c2e706e67" width="200" height="200" data-canonical-src="https://storage.googleapis.com/tensorflow-graphics/notebooks/environment_lighting/thumbnail.png" style="max-width: 100%;"> </a></td> <td align="center"><a href="https://colab.research.google.com/github/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/mesh_segmentation_demo.ipynb" rel="nofollow"><img border="0" src="https://camo.githubusercontent.com/fc840d0515043e6259d6871e0ad0303eab66a76c32229d6f9c2925a0a522f070/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f74656e736f72666c6f772d67726170686963732f6e6f7465626f6f6b732f6d6573685f7365676d656e746174696f6e2f7468756d626e61696c2e6a7067" width="200" height="200" data-canonical-src="https://storage.googleapis.com/tensorflow-graphics/notebooks/mesh_segmentation/thumbnail.jpg" style="max-width: 100%;"> </a></td> </tr> </tbody></table></markdown-accessiblity-table> </div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">TensorBoard 3D</h2><a id="user-content-tensorboard-3d" class="anchor" aria-label="Permalink: TensorBoard 3D" href="#tensorboard-3d"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Visual debugging is a great way to assess whether an experiment is going in the right direction. To this end, TensorFlow Graphics comes with a TensorBoard plugin to interactively visualize 3D meshes and point clouds. <a href="https://colab.research.google.com/github/tensorflow/tensorboard/blob/master/tensorboard/plugins/mesh/Mesh_Plugin_Tensorboard.ipynb" rel="nofollow">This demo</a> shows how to use the plugin. Follow <a href="https://github.com/tensorflow/graphics/blob/master/tensorflow_graphics/g3doc/tensorboard.md">these instructions</a> to install and configure TensorBoard 3D. Note that TensorBoard 3D is currently not compatible with eager execution nor TensorFlow 2.</p> <div align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://camo.githubusercontent.com/4e958a01aaa9d6f016b37533b911b9040b01a512393be64c5fadab21b76579a0/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f74656e736f72666c6f772d67726170686963732f6769742f726561646d652f74656e736f72626f6172645f706c7567696e2e6a7067"><img border="0" src="https://camo.githubusercontent.com/4e958a01aaa9d6f016b37533b911b9040b01a512393be64c5fadab21b76579a0/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f74656e736f72666c6f772d67726170686963732f6769742f726561646d652f74656e736f72626f6172645f706c7567696e2e6a7067" width="1280" data-canonical-src="https://storage.googleapis.com/tensorflow-graphics/git/readme/tensorboard_plugin.jpg" style="max-width: 100%;"></a> </div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Coming next...</h2><a id="user-content-coming-next" class="anchor" aria-label="Permalink: Coming next..." href="#coming-next"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Among many things, we are hoping to release resamplers, additional 3D convolution and pooling operators, and a differentiable rasterizer!</p> <p dir="auto">Follow us on <a href="https://twitter.com/_TFGraphics_" rel="nofollow">Twitter</a> to hear about the latest updates!</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Additional Information</h2><a id="user-content-additional-information" class="anchor" aria-label="Permalink: Additional Information" href="#additional-information"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">You may use this software under the <a href="https://github.com/tensorflow/graphics/blob/master/LICENSE">Apache 2.0 License</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Community</h2><a id="user-content-community" class="anchor" aria-label="Permalink: Community" href="#community"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">As part of TensorFlow, we're committed to fostering an open and welcoming environment.</p> <ul dir="auto"> <li><a href="https://stackoverflow.com/questions/tagged/tensorflow" rel="nofollow">Stack Overflow</a>: Ask or answer technical questions.</li> <li><a href="https://github.com/tensorflow/graphics/issues">GitHub</a>: Report bugs or make feature requests.</li> <li><a href="https://blog.tensorflow.org/" rel="nofollow">TensorFlow Blog</a>: Stay up to date on content from the TensorFlow team and best articles from the community.</li> <li><a href="http://youtube.com/tensorflow/" rel="nofollow">Youtube Channel</a>: Follow TensorFlow shows.</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">References</h2><a id="user-content-references" class="anchor" aria-label="Permalink: References" href="#references"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">If you use TensorFlow Graphics in your research, please reference it as:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@inproceedings{TensorflowGraphicsIO2019, author = {Oztireli, Cengiz and Valentin, Julien and Keskin, Cem and Pidlypenskyi, Pavel and Makadia, Ameesh and Sud, Avneesh and Bouaziz, Sofien}, title = {TensorFlow Graphics: Computer Graphics Meets Deep Learning}, year = {2019} }"><pre class="notranslate"><code>@inproceedings{TensorflowGraphicsIO2019, author = {Oztireli, Cengiz and Valentin, Julien and Keskin, Cem and Pidlypenskyi, Pavel and Makadia, Ameesh and Sud, Avneesh and Bouaziz, Sofien}, title = {TensorFlow Graphics: Computer Graphics Meets Deep Learning}, year = {2019} } </code></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Contact</h3><a id="user-content-contact" class="anchor" aria-label="Permalink: Contact" href="#contact"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Want to reach out? E-mail us at <a href="mailto:tf-graphics-contact@google.com">tf-graphics-contact@google.com</a>!</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Contributors - in alphabetical order</h3><a id="user-content-contributors---in-alphabetical-order" class="anchor" aria-label="Permalink: Contributors - in alphabetical order" href="#contributors---in-alphabetical-order"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Sofien Bouaziz (<a href="mailto:sofien@google.com">sofien@google.com</a>)</li> <li>Jay Busch</li> <li>Forrester Cole</li> <li>Ambrus Csaszar</li> <li>Boyang Deng</li> <li>Ariel Gordon</li> <li>Christian Häne</li> <li>Cem Keskin</li> <li>Ameesh Makadia</li> <li>Cengiz Öztireli</li> <li>Rohit Pandey</li> <li>Romain Prévost</li> <li>Pavel Pidlypenskyi</li> <li>Stefan Popov</li> <li>Konstantinos Rematas</li> <li>Omar Sanseviero</li> <li>Aviv Segal</li> <li>Avneesh Sud</li> <li>Andrea Tagliasacchi</li> <li>Anastasia Tkach</li> <li>Julien Valentin</li> <li>He Wang</li> <li>Yinda Zhang</li> </ul> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" 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<span style="background-color:#ededed !important;;width: 0.2%;" itemprop="keywords" aria-label="Other 0.2" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> </span></div> <ul class="list-style-none"> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/tensorflow/graphics/search?l=python" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#3572A5;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Python</span> <span>84.5%</span> </a> </li> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/tensorflow/graphics/search?l=jupyter-notebook" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#DA5B0B;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Jupyter Notebook</span> <span>9.4%</span> </a> </li> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/tensorflow/graphics/search?l=c%2B%2B" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#f34b7d;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">C++</span> <span>4.4%</span> </a> </li> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/tensorflow/graphics/search?l=javascript" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#f1e05a;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">JavaScript</span> <span>0.8%</span> </a> </li> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/tensorflow/graphics/search?l=cython" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#fedf5b;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Cython</span> <span>0.5%</span> </a> </li> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/tensorflow/graphics/search?l=shell" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#89e051;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Shell</span> <span>0.2%</span> </a> </li> <li class="d-inline"> <span class="d-inline-flex flex-items-center flex-nowrap text-small mr-3"> <svg style="color:#ededed;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> 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