ReSyncer: Rewiring Style-based Generator for Unified Audio-Visually Synced Facial Performer

• URL: http://arxiv.org/abs/2408.03284v1
• Date: Tue, 6 Aug 2024 16:31:45 GMT
• Title: ReSyncer: Rewiring Style-based Generator for Unified Audio-Visually Synced Facial Performer
• Authors: 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,
• Abstract summary: ReSyncer fuses motion and appearance with unified training. It supports fast personalized fine-tuning, video-driven lip-syncing, the transfer of speaking styles, and even face swapping.
• Score: 87.32518573172631
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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