Summary of Instructavatar: Text-guided Emotion and Motion Control For Avatar Generation, by Yuchi Wang et al.
InstructAvatar: Text-Guided Emotion and Motion Control for Avatar Generation
by Yuchi Wang, Junliang Guo, Jianhong Bai, Runyi Yu, Tianyu He, Xu Tan, Xu Sun, Jiang Bian
First submitted to arxiv on: 24 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 A novel text-guided approach for generating emotionally expressive 2D avatars, called InstructAvatar, is proposed to improve the realism and controllability of talking avatar generation models. The framework leverages a natural language interface to control both the emotion and facial motion of avatars, offering fine-grained control, improved interactivity, and generalizability. To facilitate training, an automatic annotation pipeline constructs an instruction-video paired dataset. A two-branch diffusion-based generator predicts avatars with audio and text instructions simultaneously. Experimental results show that InstructAvatar outperforms existing methods in emotion control, lip-sync quality, and naturalness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary InstructAvatar is a new way to make talking avatars look more realistic and respond better to what people say. It uses words to control the emotions and facial expressions of the avatar, making it look more human-like and interactive. The system trains by watching videos and listening to audio, then generates new avatars that match what’s being said or shown. |
Keywords
» Artificial intelligence » Diffusion
