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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.14283 |
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| _version_ | 1866914978198454272 |
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| author | Lin, Bin Yu, Yanzhen Ye, Jianhao Lv, Ruitao Yang, Yuguang Xie, Ruoye Yu, Pan Zhou, Hongbin |
| author_facet | Lin, Bin Yu, Yanzhen Ye, Jianhao Lv, Ruitao Yang, Yuguang Xie, Ruoye Yu, Pan Zhou, Hongbin |
| contents | Existing audio-driven facial animation methods face critical challenges, including expression leakage, ineffective subtle expression transfer, and imprecise audio-driven synchronization. We discovered that these issues stem from limitations in motion representation and the lack of fine-grained control over facial expressions. To address these problems, we present Takin-ADA, a novel two-stage approach for real-time audio-driven portrait animation. In the first stage, we introduce a specialized loss function that enhances subtle expression transfer while reducing unwanted expression leakage. The second stage utilizes an advanced audio processing technique to improve lip-sync accuracy. Our method not only generates precise lip movements but also allows flexible control over facial expressions and head motions. Takin-ADA achieves high-resolution (512x512) facial animations at up to 42 FPS on an RTX 4090 GPU, outperforming existing commercial solutions. Extensive experiments demonstrate that our model significantly surpasses previous methods in video quality, facial dynamics realism, and natural head movements, setting a new benchmark in the field of audio-driven facial animation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_14283 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Takin-ADA: Emotion Controllable Audio-Driven Animation with Canonical and Landmark Loss Optimization Lin, Bin Yu, Yanzhen Ye, Jianhao Lv, Ruitao Yang, Yuguang Xie, Ruoye Yu, Pan Zhou, Hongbin Computer Vision and Pattern Recognition Existing audio-driven facial animation methods face critical challenges, including expression leakage, ineffective subtle expression transfer, and imprecise audio-driven synchronization. We discovered that these issues stem from limitations in motion representation and the lack of fine-grained control over facial expressions. To address these problems, we present Takin-ADA, a novel two-stage approach for real-time audio-driven portrait animation. In the first stage, we introduce a specialized loss function that enhances subtle expression transfer while reducing unwanted expression leakage. The second stage utilizes an advanced audio processing technique to improve lip-sync accuracy. Our method not only generates precise lip movements but also allows flexible control over facial expressions and head motions. Takin-ADA achieves high-resolution (512x512) facial animations at up to 42 FPS on an RTX 4090 GPU, outperforming existing commercial solutions. Extensive experiments demonstrate that our model significantly surpasses previous methods in video quality, facial dynamics realism, and natural head movements, setting a new benchmark in the field of audio-driven facial animation. |
| title | Takin-ADA: Emotion Controllable Audio-Driven Animation with Canonical and Landmark Loss Optimization |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.14283 |