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Diverse Code Query Learning for Speech-Driven Facial Animation

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OA
AI
C
Chunzhi Gu
S
Shigeru Kuriyama
K
Katsuya Hotta
DOI:10.1109/TVCG.2025.3577807delete
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Abstract

Abstract

En 中文
Speech-driven facial animation aims to synthesize lip-synchronized 3D talking faces following the given speech signal. Prior methods to this task mostly focus on pursuing realism with deterministic systems, yet characterizing the potentially stochastic nature of facial motions has been to date rarely studied. While generative modeling approaches can easily handle the one-to-many mapping by repeatedly drawing samples, ensuring a diverse mode coverage of plausible facial motions on small-scale datasets remains challenging and less explored. In this article, we propose predicting multiple samples conditioned on the same audio signal and then explicitly encouraging sample diversity to address diverse facial animation synthesis. Our core insight is to guide our model to explore the expressive facial latent space with a diversity-promoting loss such that the desired latent codes for diversification can be ideally identified. To this end, building upon the rich facial prior learned with vector-quantized variational auto-encoding mechanism, our model temporally queries multiple stochastic codes which can be flexibly decoded into a diverse yet plausible set of speech-faithful facial motions. To further allow for control over different facial parts during generation, the proposed model is designed to predict different facial portions of interest in a sequential manner, and compose them to eventually form full-face motions. Our paradigm realizes both diverse and controllable facial animation synthesis in a unified formulation. We experimentally demonstrate that our method yields state-of-the-art performance both quantitatively and qualitatively, especially regarding sample diversity.
Keywords:
Diverse facial animation synthesis
audio-visual learning
facial part control

Journal

IEEE Transactions on Visualization and Computer Graphics cover
IEEE Transactions on Visualization and Computer Graphics
IF:
6.5
Papers:
357
Citations:
2.2W

Organization

T
Toyohashi University of Technology
Scholars:
2.4K
Papers: 1.8K
Citations: 1.5K
I
Iwate University
Scholars:
1.7K
Papers: 1.5K
Citations: 2
Cited Papers

Cited Papers

Learn2Talk: 3D Talking Face Learns from 2D Talking Face
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IF0
err2024-01-01
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PREAI
errYixiang Zhuang; Baoping Cheng; Yao Cheng; Yuntao Jin; Renshuai Liu; Chengyang Li; Xuan Cheng; Jing Liao; Juncong Lin
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A Deep Learning Approach for Generalized Speech Animation
err2017-07-20
err190
errOAAI
errTaylor, Sarah; Kim, Taehwan; Yue, Yisong; Mahler, Moshe; Krahe, James; Rodriguez, Anastasio Garcia; Hodgins, Jessica; Matthews, Iain
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State of the Art on Monocular 3D Face Reconstruction, Tracking, and Applications
err2018-05-22
err0
PREAI
errM. Zollhöfer; J. Thies; P. Garrido; D. Bradley; T. Beeler; P. Pérez; M. Stamminger; M. Nießner; C. Theobalt
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Learning a model of facial shape and expression from 4D scans
err2017-11-20
err413
PREAI
errLi, Tianye; Bolkart, Timo; Black, Michael J.; Li, Hao; Romero, Javier
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Imitator: Personalized Speech-driven 3D Facial Animation
err2023-10-01
err0
errOAAI
errBalamurugan Thambiraja; Ikhsanul Habibie; Sadegh Aliakbarian; Darren Cosker; Christian Theobalt; Justus Thies
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Generating Holistic 3D Human Motion from Speech
err2023-06-01
err0
errOAAI
errHongwei Yi; Hualin Liang; Yifei Liu; Qiong Cao; Yandong Wen; Timo Bolkart; Dacheng Tao; Michael J. Black
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