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class='section' id='blog'><div class='widget FeaturedPost' data-version='1' id='FeaturedPost1'> </div><div class='widget Blog' data-version='1' id='Blog1'> <div class='tensorsite-container--narrow'> <div class='tensorsite-detail'> <a aria-hidden='true' class='tensorsite-detail__next-url' hidden='true' href='https://blog.tensorflow.org/2021/08/pose-estimation-and-classification-on-edge-devices-with-MoveNet-and-TensorFlow-Lite.html'></a> <div aria-hidden='true' class='tensorsite-detail__current-url' hidden='true'>https://blog.tensorflow.org/2021/08/3d-pose-detection-with-mediapipe-blazepose-ghum-tfjs.html</div> <div aria-hidden='true' class='tensorsite-detail__tags' hidden='true'> <span>Community</span> <b class='label-divider-dot'>·</b> <span>TensorFlow.js</span> </div> <div aria-hidden='true' class='tensorsite-detail__main-image' hidden='true'> https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEju5BNMgQYeE9f8fTIExf2qW4JTveDA2sfk5HmAQOZamLVBOPUKcVKkObW_6TfgTTr08usiwi4y1gvVhQfrkAzOhQeuHRa41IN9MvfIOXGqPIsbqsFAxL1USY3o21xOSvhyphenhyphenYHGSaXe8mbA/s0/TF+image+2.gif </div> <p aria-hidden='true' class='tensorsite-detail__description' hidden='true'> <span class='tensorsite-content__info'> August 30, 2021 — </span> <em>Posted by <a href="https://www.linkedin.com/in/grishchenko-ivan">Ivan Grishchenko</a>, <a href="https://www.linkedin.com/in/bazarevsky/">Valentin Bazarevsky</a>, <a href="https://www.linkedin.com/in/eduard-gabriel-bazavan-137598bb/">Eduard Gabriel Bazavan</a>, <a href=" https://www.linkedin.com/in/na-li-02a39315/">Na Li</a>, <a href="https://twitter.com/jason_mayes">Jason Mayes</a>, Google</em> Pose detection is an important step in understanding more about the human body in videos and images. <a href="https://github.com/tensorflow/tfjs-models/tree/master/pose-detection">Our existing models</a> have supported 2D pose estimation for some time, which many of you may have already tried. Today, we are launching our first 3D model in TF.js pose-detection API. 3D pose estima… </p> <div class='tensorsite-content__subtitle'> <a href='https://blog.tensorflow.org/search?label=Community&max-results=20'> <span>Community</span> </a> <b class='label-divider-dot'>·</b> <a href='https://blog.tensorflow.org/search?label=TensorFlow.js&max-results=20'> <span>TensorFlow.js</span> </a> <b class='label-divider-dot'>·</b> <img alt='Google Article' class='community-icon' src='https://www.gstatic.com/tf_blog/images/ic_google.svg'/> </div> <div class='tensorsite-detail__title'> 3D Pose Detection with MediaPipe BlazePose GHUM and TensorFlow.js </div> <div class='tensorsite-detail__contact'> <div class='tensorsite-detail__info'> <span class='tensorsite-detail__timestamp'>August 30, 2021</span> </div> <a class='icon-link' href='https://twitter.com/intent/tweet?text=%223D Pose Detection with MediaPipe BlazePose GHUM and TensorFlow.js%22 from the TensorFlow Blog%0A%0Ahttps://blog.tensorflow.org/2021/08/3d-pose-detection-with-mediapipe-blazepose-ghum-tfjs.html' rel='noopener noreferrer' target='_blank' title='Share this post on Twitter'> <svg alt='Twitter Social Icon' class='twitter-icon social-icon' height='19' viewBox='0 0 23 19' width='23' xmlns='http://www.w3.org/2000/svg'> <g fill='none' fill-rule='evenodd' transform='translate(-7 -9)'> <rect height='36' width='36'></rect> <path d='M14.076,27.2827953 C22.566,27.2827953 27.21,20.2477953 27.21,14.1477953 C27.21,13.9477953 27.21,13.7487953 27.197,13.5507953 C28.1,12.8977953 28.88,12.0887953 29.5,11.1617953 C28.657,11.5347953 27.764,11.7797953 26.848,11.8877953 C27.812,11.3107953 28.533,10.4037953 28.878,9.33479527 C27.972,9.87179527 26.98,10.2507953 25.947,10.4547953 C24.198,8.59579527 21.274,8.50679527 19.415,10.2547953 C18.217,11.3817953 17.708,13.0617953 18.08,14.6647953 C14.368,14.4787953 10.91,12.7257953 8.566,9.84279527 C7.341,11.9507953 7.967,14.6497953 9.995,16.0047953 C9.261,15.9827953 8.542,15.7837953 7.9,15.4267953 L7.9,15.4847953 C7.9,17.6827953 9.449,19.5747953 11.603,20.0107953 C10.924,20.1957953 10.211,20.2227953 9.519,20.0897953 C10.124,21.9707953 11.856,23.2587953 13.832,23.2957953 C12.197,24.5797953 10.178,25.2777953 8.098,25.2747953 C7.731,25.2747953 7.364,25.2527953 7,25.2087953 C9.111,26.5627953 11.567,27.2817953 14.076,27.2787953' fill='#545454'></path> </g> </svg> </a> </div> <div class='divider divider--article-top'></div> <div class='tensorsite-detail__body'> <meta content="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEju5BNMgQYeE9f8fTIExf2qW4JTveDA2sfk5HmAQOZamLVBOPUKcVKkObW_6TfgTTr08usiwi4y1gvVhQfrkAzOhQeuHRa41IN9MvfIOXGqPIsbqsFAxL1USY3o21xOSvhyphenhyphenYHGSaXe8mbA/s0/TF+image+2.gif" name="twitter:image"></meta> <img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEju5BNMgQYeE9f8fTIExf2qW4JTveDA2sfk5HmAQOZamLVBOPUKcVKkObW_6TfgTTr08usiwi4y1gvVhQfrkAzOhQeuHRa41IN9MvfIOXGqPIsbqsFAxL1USY3o21xOSvhyphenhyphenYHGSaXe8mbA/s0/TF+image+2.gif" style="display: none;" /> <p> <em>Posted by <a href="https://www.linkedin.com/in/grishchenko-ivan">Ivan Grishchenko</a>, <a href="https://www.linkedin.com/in/bazarevsky/">Valentin Bazarevsky</a>, <a href="https://www.linkedin.com/in/eduard-gabriel-bazavan-137598bb/">Eduard Gabriel Bazavan</a>, <a href=" https://www.linkedin.com/in/na-li-02a39315/">Na Li</a>, <a href="https://twitter.com/jason_mayes">Jason Mayes</a>, Google</em> </p> <p> Pose detection is an important step in understanding more about the human body in videos and images. <a href="https://github.com/tensorflow/tfjs-models/tree/master/pose-detection">Our existing models</a> have supported 2D pose estimation for some time, which many of you may have already tried. </p> <p> Today, we are launching our first 3D model in TF.js pose-detection API. 3D pose estimation opens up new design opportunities for applications such as fitness, medical, motion capture and beyond - in many of these areas we’ve seen a growing interest from the TensorFlow.js community. A great example of this is <a href="https://twitter.com/yeemachine/status/1414993821583118341?s=20">3D motion capture to drive a character animation</a> in the browser. </p> <table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody><tr><td style="text-align: center;"><center><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiamTBdI8uEB6Zw0XVR3qEal7l_NhqO6WZH3KMZPjkcKYSyR0K7hI26jpcwFe_imAKppGe4GMEMzr4tIkDeZhhpjxCei82hadi1leS-E3iaw_cvE9oYhSZ85m-b5c5zJJTBQgiWERSXwf8/s0/ezgif-2-c72ec21b45eb.gif" style="display: block; padding: 1em 0; text-align: center; "><img alt="3D motion capture with BlazePose GHUM" border="0" data-original-height="600" data-original-width="600" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiamTBdI8uEB6Zw0XVR3qEal7l_NhqO6WZH3KMZPjkcKYSyR0K7hI26jpcwFe_imAKppGe4GMEMzr4tIkDeZhhpjxCei82hadi1leS-E3iaw_cvE9oYhSZ85m-b5c5zJJTBQgiWERSXwf8/s0/ezgif-2-c72ec21b45eb.gif" style="width:50%"/></a></center></td></tr> <tr><td class="tr-caption" style="text-align: center;"><p><em>3D motion capture with BlazePose GHUM by <a href="https://twitter.com/yeemachine">Richard Yee</a></em></p> <p> <em>(used with permission, live demo available at <a href="https://3d.kalidoface.com/">3d.kalidoface.com</a>)</em></p></td></tr> </tbody></table> <p> This community demo uses multiple models powered by MediaPipe and TensorFlow.js (namely FaceMesh, BlazePose and HandPose). Even better, no app install is needed as you just need to visit a webpage to enjoy the experience. So with that in mind, let’s learn more and see this new model in action! </p> <table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEju5BNMgQYeE9f8fTIExf2qW4JTveDA2sfk5HmAQOZamLVBOPUKcVKkObW_6TfgTTr08usiwi4y1gvVhQfrkAzOhQeuHRa41IN9MvfIOXGqPIsbqsFAxL1USY3o21xOSvhyphenhyphenYHGSaXe8mbA/s0/TF+image+2.gif" style="display: block; padding: 1em 0; text-align: center; "><img alt="BlazePose live demo" border="0" data-original-height="288" data-original-width="512" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEju5BNMgQYeE9f8fTIExf2qW4JTveDA2sfk5HmAQOZamLVBOPUKcVKkObW_6TfgTTr08usiwi4y1gvVhQfrkAzOhQeuHRa41IN9MvfIOXGqPIsbqsFAxL1USY3o21xOSvhyphenhyphenYHGSaXe8mbA/s0/TF+image+2.gif" style="width:100%"/></a></td></tr> <tr><td class="tr-caption" style="text-align: center;"><strong><a href="https://storage.googleapis.com/tfjs-models/demos/pose-detection/index.html?model=blazepose">Try out the live demo!</a></strong></td></tr> </tbody></table> <h2>Installation</h2> <p> The pose-detection API provides two runtimes for BlazePose GHUM, namely MediaPipe runtime and TensorFlow.js runtime. </p> <p> To install the API and runtime library, you can either use the <code><script></code> tag in your html file or use NPM. </p> <p><strong>Through script tag:</strong></p> <pre><code class=”language-python”><script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/pose-detection"></script>  <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-core"></script> <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-converter"></script> <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-webgl"></script>  <script src="https://cdn.jsdelivr.net/npm/@mediapipe/pose"></script></code></pre> <p><strong>Through NPM:</strong></p> <pre><code class=”language-python”>yarn add @tensorflow-models/pose-detection # Run below commands if you want to use TF.js runtime. yarn add @tensorflow/tfjs-core @tensorflow/tfjs-converter yarn add @tensorflow/tfjs-backend-webgl # Run below commands if you want to use MediaPipe runtime. yarn add @mediapipe/pose</code></pre> <p> To reference the API in your JS code, it depends on how you installed the library. </p> <p> If installed through script tag, you can reference the library through the global namespace <code>poseDetection</code>. </p> <p> If installed through NPM, you need to import the libraries first:<br> </p> <pre><code class=”language-python”>import * as poseDetection from '@tensorflow-models/pose-detection'; // Uncomment the line below if you want to use TF.js runtime. // import '@tensorflow/tfjs-backend-webgl'; // Uncomment the line below if you want to use MediaPipe runtime. // import '@mediapipe/pose';</code></pre> <h2>Try it yourself!</h2> <p> First, you need to create a detector:<br> </p> <pre><code class=”language-python”>const model = poseDetection.SupportedModels.BlazePose; const detectorConfig = { runtime: 'mediapipe', // or 'tfjs' modelType: 'full' }; detector = await poseDetection.createDetector(model, detectorConfig);</code></pre> <p> Choose a modelType that fits your application needs, there are three options for you to choose from: <code>lite</code>, <code>full</code>, and <code>heavy</code>. From <code>lite</code> to <code>heavy</code>, the accuracy increases while the inference speed decreases. Please try our <a href="https://storage.googleapis.com/tfjs-models/demos/pose-detection/index.html?model=blazepose">live demo</a> to compare different configurations. </p> <p> Once you have a detector, you can pass in a video stream to detect poses:<br> </p> <pre><code class=”language-python”>const video = document.getElementById('video'); const poses = await detector.estimatePoses(video);</code></pre> <p> How to use the output? <code>poses</code> represent an array of detected <code>pose</code> predictions in the image frame. For each <code>pose</code>, it contains <code>keypoints</code> and <code>keypoints3D</code>. The <code>keypoints</code> are the same as the 2D model we launched before, it is an array of 33 keypoint objects, each object has x, y in pixel units. <br><br><code>keypoints3D</code> is an additional array with 33 keypoint objects, each object has x, y, z. The x, y, z are in meter units. The person is modeled as if they were in a 2m x 2m x 2m cubic space. The range for each axis goes from -1 to 1 (therefore 2m total delta). The origin of this 3D space is the hip center (0, 0, 0). From the origin, z is positive if moving closer to the camera, and negative if moving away from the camera. See below output snippet for example: </p> <pre><code class=”language-python”>[ { score: 0.8, keypoints: [ {x: 230, y: 220, score: 0.9, name: "nose"}, {x: 212, y: 190, score: 0.8, name: "left_eye"}, ... ], keypoints3D: [ {x: 0.5, y: 0.9, z: 0.06 score: 0.9, name: "nose"}, ... ] } ]</code></pre> <p> You can refer to our <a href="https://github.com/tensorflow/tfjs-models/tree/master/pose-detection">ReadMe</a> for more details about the API. </p> <p> As you begin to play and develop with BlazePose GHUM, we would appreciate your <a href="https://github.com/tensorflow/tfjs/issues/new">feedback</a> and <a href="https://github.com/tensorflow/tfjs-models/pulls">contributions</a>. If you make something using this model, tag it with <a href="https://twitter.com/hashtag/MadeWithTFJS?src=hashtag_click&f=live">#MadeWithTFJS</a> on social media so we can find your work, as we would love to see what you create. </p> <h2>Model deep dive</h2> <p> The key challenge to build the 3D part of our pose model was obtaining realistic, in-the-wild 3D data. In contrast to 2D, which can be obtained via human annotation, accurate manual 3D annotation becomes a uniquely challenging task. It requires either a lab setup or specialised hardware with depth sensors for 3D scans - which introduce additional challenges to preserve a good level of human and environment diversity in the dataset. Another alternative, which many researchers choose - to build a completely synthetic dataset, which introduces yet another challenge of domain adaptation to real-world pictures. </p> <p> Our approach is based on a statistical 3D human body model called <a href="https://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_GHUM__GHUML_Generative_3D_Human_Shape_and_Articulated_Pose_CVPR_2020_paper.pdf">GHUM</a>, which is built using a large corpus of human shapes and motions. To obtain 3D human body pose ground truth, we fitted the GHUM model to our existing 2D pose dataset and extended it with a real world 3D keypoint coordinates in metric space. During the fitting process the shape and the pose variables of GHUM were optimized such that the reconstructed model aligns with the image evidence. This includes 2D keypoint and silhouette semantic segmentation alignment as well as shape and pose regularization terms. For more details see related work on 3D pose and shape inference (<a href="https://openaccess.thecvf.com/content/CVPR2021/papers/Zanfir_Neural_Descent_for_Visual_3D_Human_Pose_and_Shape_CVPR_2021_paper.pdf">HUND</a>, <a href="https://arxiv.org/pdf/2106.09336.pdf">THUNDR</a>). </p> <table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiPOE7MIIwTCjNC_VHUPioyu9LljcPoFsyZdBzJTqaCZn7pTz3saMprQ4aMfaDzxZEmEat9eJw2QHFCWxU8CTSuumMnr0u8O8B6kWcBvcKt259Y1KGKNb1rWUKEH6x3o5Y5tD2MULb9FuI/s0/TF+image+3.jpg" style="display: block; padding: 1em 0; text-align: center; "><img alt="Sample GHUM fitting for input image" border="0" data-original-height="171" data-original-width="512" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiPOE7MIIwTCjNC_VHUPioyu9LljcPoFsyZdBzJTqaCZn7pTz3saMprQ4aMfaDzxZEmEat9eJw2QHFCWxU8CTSuumMnr0u8O8B6kWcBvcKt259Y1KGKNb1rWUKEH6x3o5Y5tD2MULb9FuI/s0/TF+image+3.jpg" style="width:100%"/></a></td></tr> <tr><td class="tr-caption" style="text-align: center;"><em>Sample GHUM fitting for an input image. From left to right: original image, 3D GHUM reconstruction (different viewpoint) and blended result projected on top of the original image.</em></td></tr> </tbody></table> <p> Due to the nature of <a href="https://en.wikipedia.org/wiki/3D_projection">3D to 2D projection</a>, multiple points in 3D can have the same projection in 2D (i.e. with the same X and Y but different Z). So the fitting can result in several realistic 3D body poses for the given 2D annotation. To minimize this ambiguity, in addition to a 2D body pose, we asked annotators to provide depth order between pose skeleton edges where they are certain (check the figure below). This task proved to be an easy one (compared to a real depth annotation) showing high consistency between annotators (98% on cross-validation) and helped to reduce the depth ordering errors for the fitted GHUM reconstructions from 25% to 3%. </p> <table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhHa0CeczTYhy0D-ESUY0fY9znsKwpHj21XL7NkjuBt9o-IT3rzWfVee6b0OlgixPZCf4lnsSb3J7yuRR2q3RoYxldFdMb9U9Awd_Jxx1xlCNQ78XgxoEMa4Erz1ZcZq7XW0MgcUHiZqzE/s0/TF_image_4.jpeg" style="display: block; padding: 1em 0; text-align: center; "><img alt="Depth order annotation: the wider edge corner denotes the corner closer to the camera (e.g. the person’s right shoulder is closer to camera than left shoulder on both examples) " border="0" data-original-height="593" data-original-width="1280" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhHa0CeczTYhy0D-ESUY0fY9znsKwpHj21XL7NkjuBt9o-IT3rzWfVee6b0OlgixPZCf4lnsSb3J7yuRR2q3RoYxldFdMb9U9Awd_Jxx1xlCNQ78XgxoEMa4Erz1ZcZq7XW0MgcUHiZqzE/s0/TF_image_4.jpeg" style="width:100%"/></a></td></tr> <tr><td class="tr-caption" style="text-align: center;">"Depth order" annotation: the wider edge corner denotes the corner closer to the camera (e.g. the person’s right shoulder is closer to camera than left shoulder on both examples) </td></tr> </tbody></table> <p> BlazePose GHUM utilizes a two-step <a href="https://blog.tensorflow.org/2021/05/high-fidelity-pose-tracking-with-mediapipe-blazepose-and-tfjs.html">detector-tracker approach</a> where the tracker operates on a cropped human image. Thus the model is trained to predict 3D body pose in relative coordinates of a metric space with origin in the subject's hips center. </p> <h2>MediaPipe vs. TF.js runtime</h2> <p> There are some pros and cons of using each runtime. As shown in the performance table below, the MediaPipe runtime provides faster inference speed on desktop, laptop and android phones. The TF.js runtime provides faster inference speed on iPhones and iPads. The TF.js runtime is also about 1 MB smaller than the MediaPipe runtime. </p> <div align="left" dir="ltr" style="margin-left:0pt;"> <table style="border:none;border-collapse:collapse;"> <tbody> <tr style="height:0pt;"> <td style="border-left:solid #000000 1pt;border-right:solid #000000 1pt;border-bottom:solid #000000 1pt;border-top:solid #000000 1pt;vertical-align:top;padding:5pt 5pt 5pt 5pt;overflow:hidden;overflow-wrap:break-word;"><br></td> <td style="border-left:solid #000000 1pt;border-right:solid #000000 1pt;border-bottom:solid #000000 1pt;border-top:solid #000000 1pt;vertical-align:top;padding:5pt 5pt 5pt 5pt;overflow:hidden;overflow-wrap:break-word;"> <p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt;"><span style="font-size:10pt;font-family:Arial;color:#202124;background-color:transparent;font-weight:700;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre;white-space:pre-wrap;">MacBook Pro 15” 2019. </span></p> <p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt;"><span style="font-size:9pt;font-family:Arial;color:#3c4043;background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre;white-space:pre-wrap;">Intel core i9.</span><span style="font-size:10pt;font-family:Arial;color:#202124;background-color:transparent;font-weight:700;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre;white-space:pre-wrap;"> </span></p> <p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt;"><span style="font-size:9pt;font-family:Arial;color:#3c4043;background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre;white-space:pre-wrap;">AMD Radeon Pro Vega 20 Graphics.</span></p> <p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt;"><span style="font-size:9pt;font-family:Arial;color:#3c4043;background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre;white-space:pre-wrap;">(FPS)</span></p> </td> <td style="border-left:solid #000000 1pt;border-right:solid #000000 1pt;border-bottom:solid #000000 1pt;border-top:solid #000000 1pt;vertical-align:top;padding:5pt 5pt 5pt 5pt;overflow:hidden;overflow-wrap:break-word;"> <p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt;"><span style="font-size:10pt;font-family:Arial;color:#202124;background-color:transparent;font-weight:700;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre;white-space:pre-wrap;">iPhone 11</span></p> <p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt;"><span style="font-size:9pt;font-family:Arial;color:#3c4043;background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre;white-space:pre-wrap;">(FPS)</span></p> </td> <td style="border-left:solid #000000 1pt;border-right:solid #000000 1pt;border-bottom:solid #000000 1pt;border-top:solid #000000 1pt;vertical-align:top;padding:5pt 5pt 5pt 5pt;overflow:hidden;overflow-wrap:break-word;"> <p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt;"><span style="font-size:10pt;font-family:Arial;color:#202124;background-color:transparent;font-weight:700;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre;white-space:pre-wrap;">Pixel 5</span></p> <p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt;"><span style="font-size:9pt;font-family:Arial;color:#3c4043;background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre;white-space:pre-wrap;">(FPS)</span></p> </td> <td style="border-left:solid #000000 1pt;border-right:solid #000000 1pt;border-bottom:solid #000000 1pt;border-top:solid #000000 1pt;vertical-align:top;padding:5pt 5pt 5pt 5pt;overflow:hidden;overflow-wrap:break-word;"> <p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt;"><span style="font-size:10pt;font-family:Arial;color:#202124;background-color:transparent;font-weight:700;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre;white-space:pre-wrap;">Desktop </span></p> <p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt;"><span style="font-size:9pt;font-family:Arial;color:#3c4043;background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre;white-space:pre-wrap;">Intel i9-10900K. Nvidia GTX 1070 GPU.</span></p> <p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt;"><span style="font-size:9pt;font-family:Arial;color:#3c4043;background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre;white-space:pre-wrap;">(FPS)</span></p> </td> </tr> <tr style="height:0pt;"> <td style="border-left:solid #000000 1pt;border-right:solid #000000 1pt;border-bottom:solid #000000 1pt;border-top:solid #000000 1pt;vertical-align:top;padding:5pt 5pt 5pt 5pt;overflow:hidden;overflow-wrap:break-word;"> <p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt;"><span style="font-size:10pt;font-family:Arial;color:#000000;background-color:transparent;font-weight:700;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre;white-space:pre-wrap;">MediaPipe Runtime</span></p> <p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt;"><span style="font-size:9pt;font-family:Arial;color:#3c4043;background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre;white-space:pre-wrap;">With WASM & GPU Accel.</span></p> </td> <td style="border-left:solid #000000 1pt;border-right:solid #000000 1pt;border-bottom:solid #000000 1pt;border-top:solid #000000 1pt;vertical-align:top;padding:5pt 5pt 5pt 5pt;overflow:hidden;overflow-wrap:break-word;"> <p dir="ltr" style="line-height:1.2;text-align: center;margin-top:0pt;margin-bottom:0pt;"><span style="font-size:10pt;font-family:Arial;color:#202124;background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre;white-space:pre-wrap;">75 | 67 | 34</span></p> </td> <td style="border-left:solid #000000 1pt;border-right:solid #000000 1pt;border-bottom:solid #000000 1pt;border-top:solid #000000 1pt;vertical-align:top;padding:5pt 5pt 5pt 5pt;overflow:hidden;overflow-wrap:break-word;"> <p dir="ltr" style="line-height:1.2;text-align: center;margin-top:0pt;margin-bottom:0pt;"><span style="font-size:10pt;font-family:Arial;color:#202124;background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre;white-space:pre-wrap;">9 | 6 | N/A                   </span></p> </td> <td style="border-left:solid #000000 1pt;border-right:solid #000000 1pt;border-bottom:solid #000000 1pt;border-top:solid #000000 1pt;vertical-align:top;padding:5pt 5pt 5pt 5pt;overflow:hidden;overflow-wrap:break-word;"> <p dir="ltr" style="line-height:1.2;text-align: center;margin-top:0pt;margin-bottom:0pt;"><span style="font-size:10pt;font-family:Arial;color:#202124;background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre;white-space:pre-wrap;">25 | 21 | 8</span></p> </td> <td style="border-left:solid #000000 1pt;border-right:solid #000000 1pt;border-bottom:solid #000000 1pt;border-top:solid #000000 1pt;vertical-align:top;padding:5pt 5pt 5pt 5pt;overflow:hidden;overflow-wrap:break-word;"> <p dir="ltr" style="line-height:1.2;text-align: center;margin-top:0pt;margin-bottom:0pt;"><span style="font-size:10.5pt;font-family:Roboto,sans-serif;color:#202124;background-color:#ffffff;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre;white-space:pre-wrap;">150 | 130 | 97</span></p> </td> </tr> <tr style="height:0pt;"> <td style="border-left:solid #000000 1pt;border-right:solid #000000 1pt;border-bottom:solid #000000 1pt;border-top:solid #000000 1pt;vertical-align:top;padding:5pt 5pt 5pt 5pt;overflow:hidden;overflow-wrap:break-word;"> <p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt;"><span style="font-size:10pt;font-family:Arial;color:#000000;background-color:transparent;font-weight:700;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre;white-space:pre-wrap;">TFJS Runtime</span></p> <p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt;"><span style="font-size:9pt;font-family:Arial;color:#3c4043;background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre;white-space:pre-wrap;">With WebGL backend.</span></p> </td> <td style="border-left:solid #000000 1pt;border-right:solid #000000 1pt;border-bottom:solid #000000 1pt;border-top:solid #000000 1pt;vertical-align:top;padding:5pt 5pt 5pt 5pt;overflow:hidden;overflow-wrap:break-word;"> <p dir="ltr" style="line-height:1.2;text-align: center;margin-top:0pt;margin-bottom:0pt;"><span style="font-size:10pt;font-family:Arial;color:#202124;background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre;white-space:pre-wrap;">52 | 40 | 24</span></p> </td> <td style="border-left:solid #000000 1pt;border-right:solid #000000 1pt;border-bottom:solid #000000 1pt;border-top:solid #000000 1pt;vertical-align:top;padding:5pt 5pt 5pt 5pt;overflow:hidden;overflow-wrap:break-word;"> <p dir="ltr" style="line-height:1.2;text-align: center;margin-top:0pt;margin-bottom:0pt;"><span style="font-size:10pt;font-family:Arial;color:#202124;background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre;white-space:pre-wrap;"> 43 | 32 | 22</span></p> </td> <td style="border-left:solid #000000 1pt;border-right:solid #000000 1pt;border-bottom:solid #000000 1pt;border-top:solid #000000 1pt;vertical-align:top;padding:5pt 5pt 5pt 5pt;overflow:hidden;overflow-wrap:break-word;"> <p dir="ltr" style="line-height:1.2;text-align: center;margin-top:0pt;margin-bottom:0pt;"><span style="font-size:10pt;font-family:Arial;color:#202124;background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre;white-space:pre-wrap;">14 | 10 | 4</span></p> </td> <td style="border-left:solid #000000 1pt;border-right:solid #000000 1pt;border-bottom:solid #000000 1pt;border-top:solid #000000 1pt;vertical-align:top;padding:5pt 5pt 5pt 5pt;overflow:hidden;overflow-wrap:break-word;"> <p dir="ltr" style="line-height:1.2;text-align: center;margin-top:0pt;margin-bottom:0pt;"><span style="font-size:10.5pt;font-family:Roboto,sans-serif;color:#202124;background-color:transparent;font-weight:400;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline;white-space:pre;white-space:pre-wrap;">42 | 35 | 29</span></p> </td> </tr> </tbody> </table> </div> <center><p><small>Inference speed of BlazePose GHUM across different devices and runtimes. The first number in each cell is for the lite model, and the second number is for the full model, the third number is for the heavy model.</small></p></center> <h2>Acknowledgements</h2> <p> We would like to acknowledge our colleagues, who participated in creating BlazePose GHUM 3D: <a href="https://www.linkedin.com/in/andrei-zanfir-397948106/">Andrei Zanfir</a>, <a href="https://www.linkedin.com/in/cristian-sminchisescu-626b7b144/">Cristian Sminchisescu</a>, <a href="https://www.linkedin.com/in/tyler-z/">Tyler Zhu</a>, the other contributors to MediaPipe: <a href="https://www.linkedin.com/in/chuoling/">Chuo-Ling Chang</a>, <a href="https://www.linkedin.com/in/michael-hays-614b1a25/">Michael Hays</a>, <a href="https://twitter.com/realmgyong">Ming Guang Yong</a>, <a href="https://www.linkedin.com/in/matthias-grundmann-b0b0675/">Matthias Grundmann</a>, along with those involved with the TensorFlow.js pose-detection API: <a href="https://github.com/ahmedsabie">Ahmed Sabie</a> and <a href="https://www.linkedin.com/in/pyu10055/">Ping Yu</a>, and of course the community who are making amazing work with these models: <a href="https://twitter.com/yeemachine">Richard Yee</a>. </p> </div> </div> <div class='tensorsite-detail-footer'> <div class='article-divider'> <img alt='Diamond Article Divider' src='https://www.gstatic.com/tf_blog/images/ic_article_end.svg'/> </div> <a class='tensorsite-chip' href='https://blog.tensorflow.org/search?label=Community&max-results=20'> Community </a> <a class='tensorsite-chip' href='https://blog.tensorflow.org/search?label=TensorFlow.js&max-results=20'> TensorFlow.js </a> </div> </div> <div class='tensorsite-next'> <div class='tensorsite-container--large'> <div class='tensorsite-next__title'>Next post</div> <div class='tensorsite-card'> <a aria-label='Next Card' class='tensorsite-card__href next' href='https://blog.tensorflow.org/2021/08/3d-pose-detection-with-mediapipe-blazepose-ghum-tfjs.html'></a> <div class='tensorsite-content__image-wrapper'> <img alt='3D Pose Detection with MediaPipe BlazePose GHUM and TensorFlow.js' class='tensorsite-content__image' src='https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEju5BNMgQYeE9f8fTIExf2qW4JTveDA2sfk5HmAQOZamLVBOPUKcVKkObW_6TfgTTr08usiwi4y1gvVhQfrkAzOhQeuHRa41IN9MvfIOXGqPIsbqsFAxL1USY3o21xOSvhyphenhyphenYHGSaXe8mbA/s0/TF+image+2.gif'/> </div> <div class='tensorsite-content'> <div class='tensorsite-content__subtitle next'> <span>Community</span> <b class='label-divider-dot'>·</b> <span>TensorFlow.js</span> <b class='label-divider-dot'>·</b> <img alt='Google Article' class='community-icon' src='https://www.gstatic.com/tf_blog/images/ic_google.svg'/> </div> <div class='tensorsite-content__title next'> 3D Pose Detection with MediaPipe BlazePose GHUM and TensorFlow.js </div> <p class='tensorsite-content__description next'> <span class='tensorsite-content__info'> August 30, 2021 </span> — <span> <em>Posted by <a href="https://www.linkedin.com/in/grishchenko-ivan">Ivan Grishchenko</a>, <a href="https://www.linkedin.com/in/bazarevsky/">Valentin Bazarevsky</a>, <a href="https://www.linkedin.com/in/eduard-gabriel-bazavan-137598bb/">Eduard Gabriel Bazavan</a>, <a href=" https://www.linkedin.com/in/na-li-02a39315/">Na Li</a>, <a href="https://twitter.com/jason_mayes">Jason Mayes</a>, Google</em> Pose detection is an important step in understanding more about the human body in videos and images. <a href="https://github.com/tensorflow/tfjs-models/tree/master/pose-detection">Our existing models</a> have supported 2D pose estimation for some time, which many of you may have already tried. 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