ReSyncer: Rewiring Style-based Generator for Unified Audio-Visually Synced Facial Performer
<|reference_start|>ReSyncer: Rewiring Style-based Generator for Unified Audio-Visually Synced Facial Performer: Lip-syncing videos with given audio is the foundation for various applications including the creation of virtual presenters or performers. While recent studies explore high-fidelity lip-sync with different techniques, their task-orientated models either require long-term videos for clip-specific training or retain visible artifacts. In this paper, we propose a unified and effective framework ReSyncer, that synchronizes generalized audio-visual facial information. The key design is revisiting and rewiring the Style-based generator to efficiently adopt 3D facial dynamics predicted by a principled style-injected Transformer. By simply re-configuring the information insertion mechanisms within the noise and style space, our framework fuses motion and appearance with unified training. Extensive experiments demonstrate that ReSyncer not only produces high-fidelity lip-synced videos according to audio, but also supports multiple appealing properties that are suitable for creating virtual presenters and performers, including fast personalized fine-tuning, video-driven lip-syncing, the transfer of speaking styles, and even face swapping. Resources can be found at https://guanjz20.github.io/projects/ReSyncer.<|reference_end|>
@article{guan2024resyncer:,
    title={ReSyncer: Rewiring Style-based Generator for Unified Audio-Visually
  Synced Facial Performer},
    author={Jiazhi Guan, Zhiliang Xu, Hang Zhou, Kaisiyuan Wang, Shengyi He,
  Zhanwang Zhang, Borong Liang, Haocheng Feng, Errui Ding, Jingtuo Liu,
  Jingdong Wang, Youjian Zhao, Ziwei Liu},
    journal={arXiv preprint arXiv:2408.03284},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.03284},
    primaryClass={cs.CV cs.GR cs.MM}
}