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</div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.19110">arXiv:2404.19110</a> <span> [<a href="https://arxiv.org/pdf/2404.19110">pdf</a>, <a href="https://arxiv.org/format/2404.19110">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> EMOPortraits: Emotion-enhanced Multimodal One-shot Head Avatars </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Drobyshev%2C+N">Nikita Drobyshev</a>, <a href="/search/cs?searchtype=author&query=Casademunt%2C+A+B">Antoni Bigata Casademunt</a>, <a href="/search/cs?searchtype=author&query=Vougioukas%2C+K">Konstantinos Vougioukas</a>, <a href="/search/cs?searchtype=author&query=Landgraf%2C+Z">Zoe Landgraf</a>, <a href="/search/cs?searchtype=author&query=Petridis%2C+S">Stavros Petridis</a>, <a href="/search/cs?searchtype=author&query=Pantic%2C+M">Maja Pantic</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.19110v1-abstract-short" style="display: inline;"> Head avatars animated by visual signals have gained popularity, particularly in cross-driving synthesis where the driver differs from the animated character, a challenging but highly practical approach. The recently presented MegaPortraits model has demonstrated state-of-the-art results in this domain. We conduct a deep examination and evaluation of this model, with a particular focus on its laten… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.19110v1-abstract-full').style.display = 'inline'; document.getElementById('2404.19110v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.19110v1-abstract-full" style="display: none;"> Head avatars animated by visual signals have gained popularity, particularly in cross-driving synthesis where the driver differs from the animated character, a challenging but highly practical approach. The recently presented MegaPortraits model has demonstrated state-of-the-art results in this domain. We conduct a deep examination and evaluation of this model, with a particular focus on its latent space for facial expression descriptors, and uncover several limitations with its ability to express intense face motions. To address these limitations, we propose substantial changes in both training pipeline and model architecture, to introduce our EMOPortraits model, where we: Enhance the model's capability to faithfully support intense, asymmetric face expressions, setting a new state-of-the-art result in the emotion transfer task, surpassing previous methods in both metrics and quality. Incorporate speech-driven mode to our model, achieving top-tier performance in audio-driven facial animation, making it possible to drive source identity through diverse modalities, including visual signal, audio, or a blend of both. We propose a novel multi-view video dataset featuring a wide range of intense and asymmetric facial expressions, filling the gap with absence of such data in existing datasets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.19110v1-abstract-full').style.display = 'none'; document.getElementById('2404.19110v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.08854">arXiv:2305.08854</a> <span> [<a href="https://arxiv.org/pdf/2305.08854">pdf</a>, <a href="https://arxiv.org/format/2305.08854">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Laughing Matters: Introducing Laughing-Face Generation using Diffusion Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Casademunt%2C+A+B">Antoni Bigata Casademunt</a>, <a href="/search/cs?searchtype=author&query=Mira%2C+R">Rodrigo Mira</a>, <a href="/search/cs?searchtype=author&query=Drobyshev%2C+N">Nikita Drobyshev</a>, <a href="/search/cs?searchtype=author&query=Vougioukas%2C+K">Konstantinos Vougioukas</a>, <a href="/search/cs?searchtype=author&query=Petridis%2C+S">Stavros Petridis</a>, <a href="/search/cs?searchtype=author&query=Pantic%2C+M">Maja Pantic</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2305.08854v2-abstract-short" style="display: inline;"> Speech-driven animation has gained significant traction in recent years, with current methods achieving near-photorealistic results. However, the field remains underexplored regarding non-verbal communication despite evidence demonstrating its importance in human interaction. In particular, generating laughter sequences presents a unique challenge due to the intricacy and nuances of this behaviour… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.08854v2-abstract-full').style.display = 'inline'; document.getElementById('2305.08854v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.08854v2-abstract-full" style="display: none;"> Speech-driven animation has gained significant traction in recent years, with current methods achieving near-photorealistic results. However, the field remains underexplored regarding non-verbal communication despite evidence demonstrating its importance in human interaction. In particular, generating laughter sequences presents a unique challenge due to the intricacy and nuances of this behaviour. This paper aims to bridge this gap by proposing a novel model capable of generating realistic laughter sequences, given a still portrait and an audio clip containing laughter. We highlight the failure cases of traditional facial animation methods and leverage recent advances in diffusion models to produce convincing laughter videos. We train our model on a diverse set of laughter datasets and introduce an evaluation metric specifically designed for laughter. When compared with previous speech-driven approaches, our model achieves state-of-the-art performance across all metrics, even when these are re-trained for laughter generation. Our code and project are publicly available <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.08854v2-abstract-full').style.display = 'none'; document.getElementById('2305.08854v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2303.17200">arXiv:2303.17200</a> <span> [<a href="https://arxiv.org/pdf/2303.17200">pdf</a>, <a href="https://arxiv.org/format/2303.17200">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> SynthVSR: Scaling Up Visual Speech Recognition With Synthetic Supervision </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Liu%2C+X">Xubo Liu</a>, <a href="/search/cs?searchtype=author&query=Lakomkin%2C+E">Egor Lakomkin</a>, <a href="/search/cs?searchtype=author&query=Vougioukas%2C+K">Konstantinos Vougioukas</a>, <a href="/search/cs?searchtype=author&query=Ma%2C+P">Pingchuan Ma</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+H">Honglie Chen</a>, <a href="/search/cs?searchtype=author&query=Xie%2C+R">Ruiming Xie</a>, <a href="/search/cs?searchtype=author&query=Doulaty%2C+M">Morrie Doulaty</a>, <a href="/search/cs?searchtype=author&query=Moritz%2C+N">Niko Moritz</a>, <a href="/search/cs?searchtype=author&query=Kol%C3%A1%C5%99%2C+J">J谩chym Kol谩艡</a>, <a href="/search/cs?searchtype=author&query=Petridis%2C+S">Stavros Petridis</a>, <a href="/search/cs?searchtype=author&query=Pantic%2C+M">Maja Pantic</a>, <a href="/search/cs?searchtype=author&query=Fuegen%2C+C">Christian Fuegen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2303.17200v2-abstract-short" style="display: inline;"> Recently reported state-of-the-art results in visual speech recognition (VSR) often rely on increasingly large amounts of video data, while the publicly available transcribed video datasets are limited in size. In this paper, for the first time, we study the potential of leveraging synthetic visual data for VSR. Our method, termed SynthVSR, substantially improves the performance of VSR systems wit… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.17200v2-abstract-full').style.display = 'inline'; document.getElementById('2303.17200v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.17200v2-abstract-full" style="display: none;"> Recently reported state-of-the-art results in visual speech recognition (VSR) often rely on increasingly large amounts of video data, while the publicly available transcribed video datasets are limited in size. In this paper, for the first time, we study the potential of leveraging synthetic visual data for VSR. Our method, termed SynthVSR, substantially improves the performance of VSR systems with synthetic lip movements. The key idea behind SynthVSR is to leverage a speech-driven lip animation model that generates lip movements conditioned on the input speech. The speech-driven lip animation model is trained on an unlabeled audio-visual dataset and could be further optimized towards a pre-trained VSR model when labeled videos are available. As plenty of transcribed acoustic data and face images are available, we are able to generate large-scale synthetic data using the proposed lip animation model for semi-supervised VSR training. We evaluate the performance of our approach on the largest public VSR benchmark - Lip Reading Sentences 3 (LRS3). SynthVSR achieves a WER of 43.3% with only 30 hours of real labeled data, outperforming off-the-shelf approaches using thousands of hours of video. The WER is further reduced to 27.9% when using all 438 hours of labeled data from LRS3, which is on par with the state-of-the-art self-supervised AV-HuBERT method. Furthermore, when combined with large-scale pseudo-labeled audio-visual data SynthVSR yields a new state-of-the-art VSR WER of 16.9% using publicly available data only, surpassing the recent state-of-the-art approaches trained with 29 times more non-public machine-transcribed video data (90,000 hours). Finally, we perform extensive ablation studies to understand the effect of each component in our proposed method. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.17200v2-abstract-full').style.display = 'none'; document.getElementById('2303.17200v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 30 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">IEEE/CVF CVPR 2023</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2301.03396">arXiv:2301.03396</a> <span> [<a href="https://arxiv.org/pdf/2301.03396">pdf</a>, <a href="https://arxiv.org/format/2301.03396">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Diffused Heads: Diffusion Models Beat GANs on Talking-Face Generation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Stypu%C5%82kowski%2C+M">Micha艂 Stypu艂kowski</a>, <a href="/search/cs?searchtype=author&query=Vougioukas%2C+K">Konstantinos Vougioukas</a>, <a href="/search/cs?searchtype=author&query=He%2C+S">Sen He</a>, <a href="/search/cs?searchtype=author&query=Zi%C4%99ba%2C+M">Maciej Zi臋ba</a>, <a href="/search/cs?searchtype=author&query=Petridis%2C+S">Stavros Petridis</a>, <a href="/search/cs?searchtype=author&query=Pantic%2C+M">Maja Pantic</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2301.03396v2-abstract-short" style="display: inline;"> Talking face generation has historically struggled to produce head movements and natural facial expressions without guidance from additional reference videos. Recent developments in diffusion-based generative models allow for more realistic and stable data synthesis and their performance on image and video generation has surpassed that of other generative models. In this work, we present an autore… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.03396v2-abstract-full').style.display = 'inline'; document.getElementById('2301.03396v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2301.03396v2-abstract-full" style="display: none;"> Talking face generation has historically struggled to produce head movements and natural facial expressions without guidance from additional reference videos. Recent developments in diffusion-based generative models allow for more realistic and stable data synthesis and their performance on image and video generation has surpassed that of other generative models. In this work, we present an autoregressive diffusion model that requires only one identity image and audio sequence to generate a video of a realistic talking human head. Our solution is capable of hallucinating head movements, facial expressions, such as blinks, and preserving a given background. We evaluate our model on two different datasets, achieving state-of-the-art results on both of them. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.03396v2-abstract-full').style.display = 'none'; document.getElementById('2301.03396v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 January, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2104.13332">arXiv:2104.13332</a> <span> [<a href="https://arxiv.org/pdf/2104.13332">pdf</a>, <a href="https://arxiv.org/format/2104.13332">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/TCYB.2022.3162495">10.1109/TCYB.2022.3162495 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> End-to-End Video-To-Speech Synthesis using Generative Adversarial Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mira%2C+R">Rodrigo Mira</a>, <a href="/search/cs?searchtype=author&query=Vougioukas%2C+K">Konstantinos Vougioukas</a>, <a href="/search/cs?searchtype=author&query=Ma%2C+P">Pingchuan Ma</a>, <a href="/search/cs?searchtype=author&query=Petridis%2C+S">Stavros Petridis</a>, <a href="/search/cs?searchtype=author&query=Schuller%2C+B+W">Bj枚rn W. Schuller</a>, <a href="/search/cs?searchtype=author&query=Pantic%2C+M">Maja Pantic</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2104.13332v3-abstract-short" style="display: inline;"> Video-to-speech is the process of reconstructing the audio speech from a video of a spoken utterance. Previous approaches to this task have relied on a two-step process where an intermediate representation is inferred from the video, and is then decoded into waveform audio using a vocoder or a waveform reconstruction algorithm. In this work, we propose a new end-to-end video-to-speech model based… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2104.13332v3-abstract-full').style.display = 'inline'; document.getElementById('2104.13332v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2104.13332v3-abstract-full" style="display: none;"> Video-to-speech is the process of reconstructing the audio speech from a video of a spoken utterance. Previous approaches to this task have relied on a two-step process where an intermediate representation is inferred from the video, and is then decoded into waveform audio using a vocoder or a waveform reconstruction algorithm. In this work, we propose a new end-to-end video-to-speech model based on Generative Adversarial Networks (GANs) which translates spoken video to waveform end-to-end without using any intermediate representation or separate waveform synthesis algorithm. Our model consists of an encoder-decoder architecture that receives raw video as input and generates speech, which is then fed to a waveform critic and a power critic. The use of an adversarial loss based on these two critics enables the direct synthesis of raw audio waveform and ensures its realism. In addition, the use of our three comparative losses helps establish direct correspondence between the generated audio and the input video. We show that this model is able to reconstruct speech with remarkable realism for constrained datasets such as GRID, and that it is the first end-to-end model to produce intelligible speech for LRW (Lip Reading in the Wild), featuring hundreds of speakers recorded entirely `in the wild'. We evaluate the generated samples in two different scenarios -- seen and unseen speakers -- using four objective metrics which measure the quality and intelligibility of artificial speech. We demonstrate that the proposed approach outperforms all previous works in most metrics on GRID and LRW. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2104.13332v3-abstract-full').style.display = 'none'; document.getElementById('2104.13332v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 August, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 27 April, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Published in IEEE Transactions on Cybernetics (April 2022)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2102.09281">arXiv:2102.09281</a> <span> [<a href="https://arxiv.org/pdf/2102.09281">pdf</a>, <a href="https://arxiv.org/format/2102.09281">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> DINO: A Conditional Energy-Based GAN for Domain Translation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Vougioukas%2C+K">Konstantinos Vougioukas</a>, <a href="/search/cs?searchtype=author&query=Petridis%2C+S">Stavros Petridis</a>, <a href="/search/cs?searchtype=author&query=Pantic%2C+M">Maja Pantic</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2102.09281v1-abstract-short" style="display: inline;"> Domain translation is the process of transforming data from one domain to another while preserving the common semantics. Some of the most popular domain translation systems are based on conditional generative adversarial networks, which use source domain data to drive the generator and as an input to the discriminator. However, this approach does not enforce the preservation of shared semantics si… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.09281v1-abstract-full').style.display = 'inline'; document.getElementById('2102.09281v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2102.09281v1-abstract-full" style="display: none;"> Domain translation is the process of transforming data from one domain to another while preserving the common semantics. Some of the most popular domain translation systems are based on conditional generative adversarial networks, which use source domain data to drive the generator and as an input to the discriminator. However, this approach does not enforce the preservation of shared semantics since the conditional input can often be ignored by the discriminator. We propose an alternative method for conditioning and present a new framework, where two networks are simultaneously trained, in a supervised manner, to perform domain translation in opposite directions. Our method is not only better at capturing the shared information between two domains but is more generic and can be applied to a broader range of problems. The proposed framework performs well even in challenging cross-modal translations, such as video-driven speech reconstruction, for which other systems struggle to maintain correspondence. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.09281v1-abstract-full').style.display = 'none'; document.getElementById('2102.09281v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 February, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted to ICLR 2021</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2012.07657">arXiv:2012.07657</a> <span> [<a href="https://arxiv.org/pdf/2012.07657">pdf</a>, <a href="https://arxiv.org/format/2012.07657">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Haliassos%2C+A">Alexandros Haliassos</a>, <a href="/search/cs?searchtype=author&query=Vougioukas%2C+K">Konstantinos Vougioukas</a>, <a href="/search/cs?searchtype=author&query=Petridis%2C+S">Stavros Petridis</a>, <a href="/search/cs?searchtype=author&query=Pantic%2C+M">Maja Pantic</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2012.07657v3-abstract-short" style="display: inline;"> Although current deep learning-based face forgery detectors achieve impressive performance in constrained scenarios, they are vulnerable to samples created by unseen manipulation methods. Some recent works show improvements in generalisation but rely on cues that are easily corrupted by common post-processing operations such as compression. In this paper, we propose LipForensics, a detection appro… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.07657v3-abstract-full').style.display = 'inline'; document.getElementById('2012.07657v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2012.07657v3-abstract-full" style="display: none;"> Although current deep learning-based face forgery detectors achieve impressive performance in constrained scenarios, they are vulnerable to samples created by unseen manipulation methods. Some recent works show improvements in generalisation but rely on cues that are easily corrupted by common post-processing operations such as compression. In this paper, we propose LipForensics, a detection approach capable of both generalising to novel manipulations and withstanding various distortions. LipForensics targets high-level semantic irregularities in mouth movements, which are common in many generated videos. It consists in first pretraining a spatio-temporal network to perform visual speech recognition (lipreading), thus learning rich internal representations related to natural mouth motion. A temporal network is subsequently finetuned on fixed mouth embeddings of real and forged data in order to detect fake videos based on mouth movements without overfitting to low-level, manipulation-specific artefacts. Extensive experiments show that this simple approach significantly surpasses the state-of-the-art in terms of generalisation to unseen manipulations and robustness to perturbations, as well as shed light on the factors responsible for its performance. Code is available on GitHub. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.07657v3-abstract-full').style.display = 'none'; document.getElementById('2012.07657v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 August, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 December, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted at CVPR2021. Code: https://github.com/ahaliassos/LipForensics</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2001.04316">arXiv:2001.04316</a> <span> [<a href="https://arxiv.org/pdf/2001.04316">pdf</a>, <a href="https://arxiv.org/format/2001.04316">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Multimedia">cs.MM</span> </div> </div> <p class="title is-5 mathjax"> Visually Guided Self Supervised Learning of Speech Representations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Shukla%2C+A">Abhinav Shukla</a>, <a href="/search/cs?searchtype=author&query=Vougioukas%2C+K">Konstantinos Vougioukas</a>, <a href="/search/cs?searchtype=author&query=Ma%2C+P">Pingchuan Ma</a>, <a href="/search/cs?searchtype=author&query=Petridis%2C+S">Stavros Petridis</a>, <a href="/search/cs?searchtype=author&query=Pantic%2C+M">Maja Pantic</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2001.04316v2-abstract-short" style="display: inline;"> Self supervised representation learning has recently attracted a lot of research interest for both the audio and visual modalities. However, most works typically focus on a particular modality or feature alone and there has been very limited work that studies the interaction between the two modalities for learning self supervised representations. We propose a framework for learning audio represent… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2001.04316v2-abstract-full').style.display = 'inline'; document.getElementById('2001.04316v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2001.04316v2-abstract-full" style="display: none;"> Self supervised representation learning has recently attracted a lot of research interest for both the audio and visual modalities. However, most works typically focus on a particular modality or feature alone and there has been very limited work that studies the interaction between the two modalities for learning self supervised representations. We propose a framework for learning audio representations guided by the visual modality in the context of audiovisual speech. We employ a generative audio-to-video training scheme in which we animate a still image corresponding to a given audio clip and optimize the generated video to be as close as possible to the real video of the speech segment. Through this process, the audio encoder network learns useful speech representations that we evaluate on emotion recognition and speech recognition. We achieve state of the art results for emotion recognition and competitive results for speech recognition. This demonstrates the potential of visual supervision for learning audio representations as a novel way for self-supervised learning which has not been explored in the past. The proposed unsupervised audio features can leverage a virtually unlimited amount of training data of unlabelled audiovisual speech and have a large number of potentially promising applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2001.04316v2-abstract-full').style.display = 'none'; document.getElementById('2001.04316v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 February, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 13 January, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted at ICASSP 2020 v2: Updated to the ICASSP 2020 camera ready version</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1912.05833">arXiv:1912.05833</a> <span> [<a href="https://arxiv.org/pdf/1912.05833">pdf</a>, <a href="https://arxiv.org/format/1912.05833">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Speech-driven facial animation using polynomial fusion of features </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kefalas%2C+T">Triantafyllos Kefalas</a>, <a href="/search/cs?searchtype=author&query=Vougioukas%2C+K">Konstantinos Vougioukas</a>, <a href="/search/cs?searchtype=author&query=Panagakis%2C+Y">Yannis Panagakis</a>, <a href="/search/cs?searchtype=author&query=Petridis%2C+S">Stavros Petridis</a>, <a href="/search/cs?searchtype=author&query=Kossaifi%2C+J">Jean Kossaifi</a>, <a href="/search/cs?searchtype=author&query=Pantic%2C+M">Maja Pantic</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1912.05833v2-abstract-short" style="display: inline;"> Speech-driven facial animation involves using a speech signal to generate realistic videos of talking faces. Recent deep learning approaches to facial synthesis rely on extracting low-dimensional representations and concatenating them, followed by a decoding step of the concatenated vector. This accounts for only first-order interactions of the features and ignores higher-order interactions. In th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1912.05833v2-abstract-full').style.display = 'inline'; document.getElementById('1912.05833v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1912.05833v2-abstract-full" style="display: none;"> Speech-driven facial animation involves using a speech signal to generate realistic videos of talking faces. Recent deep learning approaches to facial synthesis rely on extracting low-dimensional representations and concatenating them, followed by a decoding step of the concatenated vector. This accounts for only first-order interactions of the features and ignores higher-order interactions. In this paper we propose a polynomial fusion layer that models the joint representation of the encodings by a higher-order polynomial, with the parameters modelled by a tensor decomposition. We demonstrate the suitability of this approach through experiments on generated videos evaluated on a range of metrics on video quality, audiovisual synchronisation and generation of blinks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1912.05833v2-abstract-full').style.display = 'none'; document.getElementById('1912.05833v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 February, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 12 December, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1906.06337">arXiv:1906.06337</a> <span> [<a href="https://arxiv.org/pdf/1906.06337">pdf</a>, <a href="https://arxiv.org/format/1906.06337">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> Realistic Speech-Driven Facial Animation with GANs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Vougioukas%2C+K">Konstantinos Vougioukas</a>, <a href="/search/cs?searchtype=author&query=Petridis%2C+S">Stavros Petridis</a>, <a href="/search/cs?searchtype=author&query=Pantic%2C+M">Maja Pantic</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1906.06337v1-abstract-short" style="display: inline;"> Speech-driven facial animation is the process that automatically synthesizes talking characters based on speech signals. The majority of work in this domain creates a mapping from audio features to visual features. This approach often requires post-processing using computer graphics techniques to produce realistic albeit subject dependent results. We present an end-to-end system that generates vid… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1906.06337v1-abstract-full').style.display = 'inline'; document.getElementById('1906.06337v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1906.06337v1-abstract-full" style="display: none;"> Speech-driven facial animation is the process that automatically synthesizes talking characters based on speech signals. The majority of work in this domain creates a mapping from audio features to visual features. This approach often requires post-processing using computer graphics techniques to produce realistic albeit subject dependent results. We present an end-to-end system that generates videos of a talking head, using only a still image of a person and an audio clip containing speech, without relying on handcrafted intermediate features. Our method generates videos which have (a) lip movements that are in sync with the audio and (b) natural facial expressions such as blinks and eyebrow movements. Our temporal GAN uses 3 discriminators focused on achieving detailed frames, audio-visual synchronization, and realistic expressions. We quantify the contribution of each component in our model using an ablation study and we provide insights into the latent representation of the model. The generated videos are evaluated based on sharpness, reconstruction quality, lip-reading accuracy, synchronization as well as their ability to generate natural blinks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1906.06337v1-abstract-full').style.display = 'none'; document.getElementById('1906.06337v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 June, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">arXiv admin note: text overlap with arXiv:1805.09313</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1906.06301">arXiv:1906.06301</a> <span> [<a href="https://arxiv.org/pdf/1906.06301">pdf</a>, <a href="https://arxiv.org/format/1906.06301">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> </div> </div> <p class="title is-5 mathjax"> Video-Driven Speech Reconstruction using Generative Adversarial Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Vougioukas%2C+K">Konstantinos Vougioukas</a>, <a href="/search/cs?searchtype=author&query=Ma%2C+P">Pingchuan Ma</a>, <a href="/search/cs?searchtype=author&query=Petridis%2C+S">Stavros Petridis</a>, <a href="/search/cs?searchtype=author&query=Pantic%2C+M">Maja Pantic</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1906.06301v1-abstract-short" style="display: inline;"> Speech is a means of communication which relies on both audio and visual information. The absence of one modality can often lead to confusion or misinterpretation of information. In this paper we present an end-to-end temporal model capable of directly synthesising audio from silent video, without needing to transform to-and-from intermediate features. Our proposed approach, based on GANs is capab… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1906.06301v1-abstract-full').style.display = 'inline'; document.getElementById('1906.06301v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1906.06301v1-abstract-full" style="display: none;"> Speech is a means of communication which relies on both audio and visual information. The absence of one modality can often lead to confusion or misinterpretation of information. In this paper we present an end-to-end temporal model capable of directly synthesising audio from silent video, without needing to transform to-and-from intermediate features. Our proposed approach, based on GANs is capable of producing natural sounding, intelligible speech which is synchronised with the video. The performance of our model is evaluated on the GRID dataset for both speaker dependent and speaker independent scenarios. To the best of our knowledge this is the first method that maps video directly to raw audio and the first to produce intelligible speech when tested on previously unseen speakers. We evaluate the synthesised audio not only based on the sound quality but also on the accuracy of the spoken words. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1906.06301v1-abstract-full').style.display = 'none'; document.getElementById('1906.06301v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 June, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1805.09313">arXiv:1805.09313</a> <span> [<a href="https://arxiv.org/pdf/1805.09313">pdf</a>, <a href="https://arxiv.org/format/1805.09313">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> End-to-End Speech-Driven Facial Animation with Temporal GANs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Vougioukas%2C+K">Konstantinos Vougioukas</a>, <a href="/search/cs?searchtype=author&query=Petridis%2C+S">Stavros Petridis</a>, <a href="/search/cs?searchtype=author&query=Pantic%2C+M">Maja Pantic</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1805.09313v4-abstract-short" style="display: inline;"> Speech-driven facial animation is the process which uses speech signals to automatically synthesize a talking character. The majority of work in this domain creates a mapping from audio features to visual features. This often requires post-processing using computer graphics techniques to produce realistic albeit subject dependent results. We present a system for generating videos of a talking head… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1805.09313v4-abstract-full').style.display = 'inline'; document.getElementById('1805.09313v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1805.09313v4-abstract-full" style="display: none;"> Speech-driven facial animation is the process which uses speech signals to automatically synthesize a talking character. The majority of work in this domain creates a mapping from audio features to visual features. This often requires post-processing using computer graphics techniques to produce realistic albeit subject dependent results. We present a system for generating videos of a talking head, using a still image of a person and an audio clip containing speech, that doesn't rely on any handcrafted intermediate features. To the best of our knowledge, this is the first method capable of generating subject independent realistic videos directly from raw audio. Our method can generate videos which have (a) lip movements that are in sync with the audio and (b) natural facial expressions such as blinks and eyebrow movements. We achieve this by using a temporal GAN with 2 discriminators, which are capable of capturing different aspects of the video. The effect of each component in our system is quantified through an ablation study. The generated videos are evaluated based on their sharpness, reconstruction quality, and lip-reading accuracy. Finally, a user study is conducted, confirming that temporal GANs lead to more natural sequences than a static GAN-based approach. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1805.09313v4-abstract-full').style.display = 'none'; document.getElementById('1805.09313v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 July, 2018; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 23 May, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2018. </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div 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