Summary of Fada: Fast Diffusion Avatar Synthesis with Mixed-supervised Multi-cfg Distillation, by Tianyun Zhong et al.
FADA: Fast Diffusion Avatar Synthesis with Mixed-Supervised Multi-CFG Distillation
by Tianyun Zhong, Chao Liang, Jianwen Jiang, Gaojie Lin, Jiaqi Yang, Zhou Zhao
First submitted to arxiv on: 22 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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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 proposes a novel method called FADA (Fast Diffusion Avatar Synthesis with Mixed-Supervised Multi-CFG Distillation) to address the limitations of diffusion-based audio-driven talking avatar methods. The main challenge is their slow inference speed, which hinders practical applications. To overcome this issue, the authors develop a mixed-supervised loss and a multi-CFG distillation approach with learnable tokens. These innovations enable FADA to generate high-fidelity videos comparable to recent diffusion model-based methods while achieving a significant speedup of 4.17-12.5 times. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FADA is a new way to make talking avatars that are more like real people. Right now, making these avatars takes too long and isn’t very good at handling different situations. The creators of FADA wanted to change this by making the process faster and better. They came up with two main ideas: a special type of training called mixed-supervised learning and a way to distill information from a teacher model. These innovations make FADA faster and more realistic than other methods, while still producing good results. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Distillation » Inference » Supervised » Teacher model
