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|a 10.1109/TVCG.2022.3230541
|2 doi
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|a DE-627
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|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Chai, Yujin
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Personalized Audio-Driven 3D Facial Animation via Style-Content Disentanglement
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|c 2024
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 31.01.2024
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|a Date Revised 06.01.2025
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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| 520 |
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|a We present a learning-based approach for generating 3D facial animations with the motion style of a specific subject from arbitrary audio inputs. The subject style is learned from a video clip (1-2 minutes) either downloaded from the Internet or captured through an ordinary camera. Traditional methods often require many hours of the subject's video to learn a robust audio-driven model and are thus unsuitable for this task. Recent research efforts aim to train a model from video collections of a few subjects but ignore the discrimination between the subject style and underlying speech content within facial motions, leading to inaccurate style or articulation. To solve the problem, we propose a novel framework that disentangles subject-specific style and speech content from facial motions. The disentanglement is enabled by two novel training mechanisms. One is two-pass style swapping between two random subjects, and the other is joint training of the decomposition network and audio-to-motion network with a shared decoder. After training, the disentangled style is combined with arbitrary audio inputs to generate stylized audio-driven 3D facial animations. Compared with start-of-the-art methods, our approach achieves better results qualitatively and quantitatively, especially in difficult cases like bilabial plosive and bilabial nasal phonemes
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| 650 |
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|a Journal Article
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| 650 |
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4 |
|a Research Support, U.S. Gov't, Non-P.H.S.
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| 650 |
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4 |
|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Shao, Tianjia
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Weng, Yanlin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhou, Kun
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on visualization and computer graphics
|d 1996
|g 30(2024), 3 vom: 19. März, Seite 1803-1820
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|x 1941-0506
|7 nnas
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| 773 |
1 |
8 |
|g volume:30
|g year:2024
|g number:3
|g day:19
|g month:03
|g pages:1803-1820
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|u http://dx.doi.org/10.1109/TVCG.2022.3230541
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