Summary of High-fidelity and Lip-synced Talking Face Synthesis Via Landmark-based Diffusion Model, by Weizhi Zhong et al.
High-fidelity and Lip-synced Talking Face Synthesis via Landmark-based Diffusion Model
by Weizhi Zhong, Junfan Lin, Peixin Chen, 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 a novel landmark-based diffusion model for generating talking faces in video. The approach leverages facial landmarks as intermediate representations to better preserve appearance details while enabling end-to-end optimization. The method involves establishing a less ambiguous mapping from audio to landmark motion of lip and jaw, followed by the use of a TalkFormer conditioning module that aligns synthesized motion with motion represented by landmarks via differentiable cross-attention. This allows for improved lip synchronization. The model also employs implicit feature warping to align reference image features with target motion, preserving more appearance details. Experimental results demonstrate that the approach can synthesize high-fidelity and lip-synced talking face videos. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to make videos of people talking by using special points on their faces called landmarks. It’s like taking a bunch of snapshots of someone’s face while they’re speaking, but it looks more natural than old methods that tried to directly turn sound into images. The new approach uses these landmarks as middle steps to help the computer learn how to make the videos look good and sound like the person is really talking. It also has special tricks to make sure the mouth moves correctly when someone is speaking. The results are pretty cool – it looks like real people talking! |
Keywords
» Artificial intelligence » Cross attention » Diffusion model » Optimization
