Summary of Nldf: Neural Light Dynamic Fields For Efficient 3d Talking Head Generation, by Niu Guanchen
NLDF: Neural Light Dynamic Fields for Efficient 3D Talking Head Generation
by Niu Guanchen
First submitted to arxiv on: 17 Jun 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 A novel Neural Light Dynamic Fields (NLDF) model is proposed to generate high-quality 3D talking faces at a significantly faster speed than the existing Neural Radiation Fields (NeRF) approach. The NLDF represents light fields based on light segments and leverages deep networks to learn the entire light beam’s information simultaneously. To improve training efficiency, knowledge distillation is applied, utilizing NeRF-based synthesized results to guide correct coloration of light segments in NLDF. Additionally, an active pool training strategy is introduced to focus on high-frequency movements, particularly on the speaker’s mouth and eyebrows. This approach effectively captures facial light dynamics in 3D talking video generation, achieving approximately 30 times faster speed compared to state-of-the-art NeRF-based methods while maintaining comparable visual quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make realistic 3D videos of people talking is introduced. The current method, called Neural Radiation Fields (NeRF), can create amazing results but takes a very long time to process. To fix this issue, researchers developed a new model called Neural Light Dynamic Fields (NLDF). This new approach works by breaking down light into smaller parts and using computers to learn how these parts work together to create the final image. The NLDF method is much faster than NeRF and still produces great results. |
Keywords
» Artificial intelligence » Knowledge distillation
