EditEmoTalk: Controllable Speech-Driven 3D Facial Animation with Continuous Expression Editing
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866911377111646208 |
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| author | Jiang, Diqiong Zhu, Kai Song, Dan Chang, Jian Chen, Chenglizhao Wu, Zhenyu |
| author_facet | Jiang, Diqiong Zhu, Kai Song, Dan Chang, Jian Chen, Chenglizhao Wu, Zhenyu |
| contents | Speech-driven 3D facial animation aims to generate realistic and expressive facial motions directly from audio. While recent methods achieve high-quality lip synchronization, they often rely on discrete emotion categories, limiting continuous and fine-grained emotional control. We present EditEmoTalk, a controllable speech-driven 3D facial animation framework with continuous emotion editing. The key idea is a boundary-aware semantic embedding that learns the normal directions of inter-emotion decision boundaries, enabling a continuous expression manifold for smooth emotion manipulation. Moreover, we introduce an emotional consistency loss that enforces semantic alignment between the generated motion dynamics and the target emotion embedding through a mapping network, ensuring faithful emotional expression. Extensive experiments demonstrate that EditEmoTalk achieves superior controllability, expressiveness, and generalization while maintaining accurate lip synchronization. Code and pretrained models will be released. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_10000 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | EditEmoTalk: Controllable Speech-Driven 3D Facial Animation with Continuous Expression Editing Jiang, Diqiong Zhu, Kai Song, Dan Chang, Jian Chen, Chenglizhao Wu, Zhenyu Multimedia Computer Vision and Pattern Recognition Speech-driven 3D facial animation aims to generate realistic and expressive facial motions directly from audio. While recent methods achieve high-quality lip synchronization, they often rely on discrete emotion categories, limiting continuous and fine-grained emotional control. We present EditEmoTalk, a controllable speech-driven 3D facial animation framework with continuous emotion editing. The key idea is a boundary-aware semantic embedding that learns the normal directions of inter-emotion decision boundaries, enabling a continuous expression manifold for smooth emotion manipulation. Moreover, we introduce an emotional consistency loss that enforces semantic alignment between the generated motion dynamics and the target emotion embedding through a mapping network, ensuring faithful emotional expression. Extensive experiments demonstrate that EditEmoTalk achieves superior controllability, expressiveness, and generalization while maintaining accurate lip synchronization. Code and pretrained models will be released. |
| title | EditEmoTalk: Controllable Speech-Driven 3D Facial Animation with Continuous Expression Editing |
| topic | Multimedia Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.10000 |