Summary of Landmark-guided Diffusion Model For High-fidelity and Temporally Coherent Talking Head Generation, by Jintao Tan et al.
Landmark-guided Diffusion Model for High-fidelity and Temporally Coherent Talking Head Generation
by Jintao Tan, Xize Cheng, Lingyu Xiong, Lei Zhu, Xiandong Li, Xianjia Wu, Kai Gong, Minglei Li, Yi Cai
First submitted to arxiv on: 3 Aug 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 proposed two-stage diffusion-based model tackles the challenging task of generating audio-driven talking heads with synchronized facial landmarks and high-quality frames. The first stage focuses on generating accurate facial landmarks based on given speech, while the second stage utilizes these landmarks as a condition to optimize mouth jitter issues and produce temporally coherent videos. Experiments show that this approach yields the best performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of creating talking heads is being developed. Right now, there are two main approaches: one focuses on lip shapes matching the audio, but neglects frame quality; the other prioritizes high-quality frames, but doesn’t care about lip shape matching. This can result in jumpy mouth movements. To fix this, researchers created a new model that has two stages. The first stage generates facial landmarks based on the audio, and then these landmarks help create talking head videos with synchronized lips and high-quality frames. The results show that this new method works better than existing approaches. |
Keywords
» Artificial intelligence » Diffusion
