EMO: Emote Portrait Alive - Generating Expressive Portrait Videos with Audio2Video Diffusion Model under Weak Conditions

• URL: http://arxiv.org/abs/2402.17485v1
• Date: Tue, 27 Feb 2024 13:10:11 GMT
• Title: EMO: Emote Portrait Alive - Generating Expressive Portrait Videos with Audio2Video Diffusion Model under Weak Conditions
• Authors: Linrui Tian, Qi Wang, Bang Zhang, Liefeng Bo
• Abstract summary: We propose EMO, a novel framework that utilizes a direct audio-to-video synthesis approach. Our method ensures seamless frame transitions and consistent identity preservation throughout the video, resulting in highly expressive and lifelike animations.
• Score: 20.062289952818666
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we tackle the challenge of enhancing the realism and expressiveness in talking head video generation by focusing on the dynamic and nuanced relationship between audio cues and facial movements. We identify the limitations of traditional techniques that often fail to capture the full spectrum of human expressions and the uniqueness of individual facial styles. To address these issues, we propose EMO, a novel framework that utilizes a direct audio-to-video synthesis approach, bypassing the need for intermediate 3D models or facial landmarks. Our method ensures seamless frame transitions and consistent identity preservation throughout the video, resulting in highly expressive and lifelike animations. Experimental results demonsrate that EMO is able to produce not only convincing speaking videos but also singing videos in various styles, significantly outperforming existing state-of-the-art methodologies in terms of expressiveness and realism.

Related papers

• EmoFace: Audio-driven Emotional 3D Face Animation [3.573880705052592]
  EmoFace is a novel audio-driven methodology for creating facial animations with vivid emotional dynamics. Our approach can generate facial expressions with multiple emotions, and has the ability to generate random yet natural blinks and eye movements. Our proposed methodology can be applied in producing dialogues animations of non-playable characters in video games, and driving avatars in virtual reality environments.
  arXiv  Detail & Related papers  (2024-07-17T11:32:16Z)
• CSTalk: Correlation Supervised Speech-driven 3D Emotional Facial Animation Generation [13.27632316528572]
  Speech-driven 3D facial animation technology has been developed for years, but its practical application still lacks expectations. Main challenges lie in data limitations, lip alignment, and the naturalness of facial expressions. This paper proposes a method called CSTalk that models the correlations among different regions of facial movements and supervises the training of the generative model to generate realistic expressions.
  arXiv  Detail & Related papers  (2024-04-29T11:19:15Z)
• VASA-1: Lifelike Audio-Driven Talking Faces Generated in Real Time [35.43018966749148]
  We introduce VASA, a framework for generating lifelike talking faces with appealing visual affective skills (VAS) given a single static image and a speech audio clip. VASA-1 is capable of not only producing lip movements that are exquisitely synchronized with the audio, but also capturing a large spectrum of facial nuances and natural head motions.
  arXiv  Detail & Related papers  (2024-04-16T15:43:22Z)
• DREAM-Talk: Diffusion-based Realistic Emotional Audio-driven Method for Single Image Talking Face Generation [75.90730434449874]
  We introduce DREAM-Talk, a two-stage diffusion-based audio-driven framework, tailored for generating diverse expressions and accurate lip-sync concurrently. Given the strong correlation between lip motion and audio, we then refine the dynamics with enhanced lip-sync accuracy using audio features and emotion style. Both quantitatively and qualitatively, DREAM-Talk outperforms state-of-the-art methods in terms of expressiveness, lip-sync accuracy and perceptual quality.
  arXiv  Detail & Related papers  (2023-12-21T05:03:18Z)
• Emotional Speech-Driven Animation with Content-Emotion Disentanglement [51.34635009347183]
  We propose EMOTE, which generates 3D talking-head avatars that maintain lip-sync from speech while enabling explicit control over the expression of emotion. EmOTE produces speech-driven facial animations with better lip-sync than state-of-the-art methods trained on the same data.
  arXiv  Detail & Related papers  (2023-06-15T09:31:31Z)
• Diffused Heads: Diffusion Models Beat GANs on Talking-Face Generation [54.68893964373141]
  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. 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.
  arXiv  Detail & Related papers  (2023-01-06T14:16:54Z)
• MeshTalk: 3D Face Animation from Speech using Cross-Modality Disentanglement [142.9900055577252]
  We propose a generic audio-driven facial animation approach that achieves highly realistic motion synthesis results for the entire face. Our approach ensures highly accurate lip motion, while also plausible animation of the parts of the face that are uncorrelated to the audio signal, such as eye blinks and eye brow motion.
  arXiv  Detail & Related papers  (2021-04-16T17:05:40Z)
• Audio-Driven Emotional Video Portraits [79.95687903497354]
  We present Emotional Video Portraits (EVP), a system for synthesizing high-quality video portraits with vivid emotional dynamics driven by audios. Specifically, we propose the Cross-Reconstructed Emotion Disentanglement technique to decompose speech into two decoupled spaces. With the disentangled features, dynamic 2D emotional facial landmarks can be deduced. Then we propose the Target-Adaptive Face Synthesis technique to generate the final high-quality video portraits.
  arXiv  Detail & Related papers  (2021-04-15T13:37:13Z)
• Audio-driven Talking Face Video Generation with Learning-based Personalized Head Pose [67.31838207805573]
  We propose a deep neural network model that takes an audio signal A of a source person and a short video V of a target person as input. We outputs a synthesized high-quality talking face video with personalized head pose. Our method can generate high-quality talking face videos with more distinguishing head movement effects than state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-02-24T10:02:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.