Summary of Lokitalk: Learning Fine-grained and Generalizable Correspondences to Enhance Nerf-based Talking Head Synthesis, by Tianqi Li et al.
LokiTalk: Learning Fine-Grained and Generalizable Correspondences to Enhance NeRF-based Talking Head Synthesis
by Tianqi Li, Ruobing Zheng, Bonan Li, Zicheng Zhang, Meng Wang, Jingdong Chen, Ming Yang
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 LokiTalk framework enhances NeRF-based talking heads by introducing Region-Specific Deformation Fields, which decompose portrait motion into lip movements, eye blinking, head pose, and torso movements. This hierarchical modeling approach improves dynamic accuracy while minimizing synthetic artifacts. Additionally, the ID-Aware Knowledge Transfer module learns generalizable dynamic and static correspondences from multi-identity videos, refining individual character depiction. Comprehensive evaluations demonstrate superior high-fidelity results and training efficiency compared to previous methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LokiTalk is a new way to make talking heads that look more realistic. It works by breaking down the movement of someone’s face into smaller parts, like lip movements and eye blinking. This helps get rid of fake-looking artifacts. The system also learns from lots of videos of different people, which helps it make each person look more unique. Overall, LokiTalk makes talking heads that are more realistic and easier to train. |
