Summary of Anitalker: Animate Vivid and Diverse Talking Faces Through Identity-decoupled Facial Motion Encoding, by Tao Liu et al.
AniTalker: Animate Vivid and Diverse Talking Faces through Identity-Decoupled Facial Motion Encoding
by Tao Liu, Feilong Chen, Shuai Fan, Chenpeng Du, Qi Chen, Xie Chen, Kai Yu
First submitted to arxiv on: 6 May 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 The paper presents AniTalker, a novel framework for generating lifelike talking faces from a single portrait. Unlike existing models that focus on lip synchronization, AniTalker captures complex facial expressions and nonverbal cues using a universal motion representation. This approach involves two self-supervised learning strategies to learn subtle motion representations and develop an identity encoder that minimizes mutual information with the motion encoder. The framework also integrates a diffusion model with a variance adapter for generating diverse and controllable facial animations. AniTalker demonstrates its capability to create detailed and realistic facial movements, highlighting its potential in crafting dynamic avatars for real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to make talking faces from just one picture. Unlike other models that mainly focus on lip movements, this framework called AniTalker can capture all sorts of facial expressions and nonverbal cues. It uses a special way to represent motion and two learning strategies to make sure the results are realistic and varied. The goal is to create dynamic avatars for real-world applications like video games or virtual assistants. |
Keywords
» Artificial intelligence » Diffusion model » Encoder » Self supervised
