Summary of Personatalk: Bring Attention to Your Persona in Visual Dubbing, by Longhao Zhang et al.
PersonaTalk: Bring Attention to Your Persona in Visual Dubbing
by Longhao Zhang, Shuang Liang, Zhipeng Ge, Tianshu Hu
First submitted to arxiv on: 9 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR)
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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 presents PersonaTalk, an attention-based two-stage framework for high-fidelity and personalized visual dubbing. The first stage uses a style-aware audio encoding module to inject speaking style into audio features through cross-attention. These stylized audio features drive speaker’s template geometry to obtain lip-synced geometries. In the second stage, a dual-attention face renderer renders textures for target geometries using Lip-Attention and Face-Attention parallel cross-attention layers. The framework preserves intricate facial details and outperforms state-of-the-art methods in terms of visual quality, lip-sync accuracy, and persona preservation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PersonaTalk is an AI system that helps create realistic videos with dubbed audio. It’s hard to make the speaker’s face match their voice because most systems don’t capture their unique style or facial details. This paper presents a new way to do this using two stages: first, it takes in audio and makes it fit the speaker’s style; then, it uses that information to create a realistic face with lip-synced movements. The result is a more natural-looking video. |
Keywords
» Artificial intelligence » Attention » Cross attention
