Summary of Style-preserving Lip Sync Via Audio-aware Style Reference, by Weizhi Zhong et al.
Style-Preserving Lip Sync via Audio-Aware Style Reference
by Weizhi Zhong, Jichang Li, Yinqi Cai, Liang Lin, Guanbin Li
First submitted to arxiv on: 10 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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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 This paper proposes an innovative audio-aware style reference scheme for audio-driven lip sync, addressing the challenge of personalized speaking styles. Earlier methods bypassed modeling these styles, leading to sub-optimal results. Recent techniques aggregate information from a style reference video but struggle with accuracy. The proposed approach uses a Transformer-based model to predict lip motion corresponding to input audio, incorporating style information aggregated through cross-attention layers. A conditional latent diffusion model is then used to render the lip motion into realistic talking face videos. Experimental results validate the efficacy of this approach in achieving precise lip sync, preserving speaking styles, and generating high-fidelity talking face videos. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make videos that show people talking with matching mouth movements when listening to audio. Right now, these videos don’t always look realistic because people have different ways of speaking. The researchers came up with a new method to make better talking face videos by paying attention to the way someone speaks and incorporating that into the video. They used special computer models to do this and tested their approach on various audio samples. The results showed that their method works well in creating realistic talking face videos. |
Keywords
» Artificial intelligence » Attention » Cross attention » Diffusion model » Transformer
