Summary of Emotivetalk: Expressive Talking Head Generation Through Audio Information Decoupling and Emotional Video Diffusion, by Haotian Wang et al.
EmotiveTalk: Expressive Talking Head Generation through Audio Information Decoupling and Emotional Video Diffusion
by Haotian Wang, Yuzhe Weng, Yueyan Li, Zilu Guo, Jun Du, Shutong Niu, Jiefeng Ma, Shan He, Xiaoyan Wu, Qiming Hu, Bing Yin, Cong Liu, Qingfeng Liu
First submitted to arxiv on: 23 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel framework, EmotiveTalk, is proposed to overcome challenges in expressiveness, controllability, and stability in long-time talking head generation using diffusion models. The framework consists of a Vision-guided Audio Information Decoupling (V-AID) approach and a Diffusion-based Co-speech Temporal Expansion (Di-CTE) module. V-AID generates audio-based decoupled representations aligned with lip movements and expression, while Di-CTE achieves alignment between audio and facial expression representation spaces under multi-source emotion condition constraints. The Emotional Talking Head Diffusion (ETHD) backbone integrates an Expression Decoupling Injection (EDI) module to automatically decouple expressions from reference portraits and integrate target expression information, resulting in highly expressive talking head videos. Experimental results demonstrate state-of-the-art performance compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Talking heads can now express emotions more naturally! Researchers created a new way to make these digital faces look and sound like they’re really feeling something. They used special algorithms called diffusion models, which were originally good at making talking heads, but had some problems. The new method, called EmotiveTalk, fixes those issues by letting the algorithm control how the face moves and what emotions are shown. It even makes sure the face stays expressive over a long time! The results are super realistic and better than other methods. This could be useful for making more believable characters in movies or videos. |
Keywords
» Artificial intelligence » Alignment » Diffusion
