Geometry-guided dense perspective network for speech-driven facial animation

Liu, Jingying, Hui, Binyuan, Li, Kun, Liu, Yunke, Lai, Yu-Kun ORCID: https://orcid.org/0000-0002-2094-5680, Zhang, Yuxiang, Liu, Yebin and Yang, Jingyu 2022. Geometry-guided dense perspective network for speech-driven facial animation. IEEE Transactions on Visualization and Computer Graphics 28 (12) , pp. 4873-4886. 10.1109/TVCG.2021.3107669

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Abstract

Realistic speech-driven 3D facial animation is a challenging problem due to the complex relationship between speech and face. In this paper, we propose a deep architecture, called Geometry-guided Dense Perspective Network (GDPnet), to achieve speaker-independent realistic 3D facial animation. The encoder is designed with dense connections to strengthen feature propagation and encourage the re-use of audio features, and the decoder is integrated with an attention mechanism to adaptively recalibrate point-wise feature responses by explicitly modeling interdependencies between different neuron units. We also introduce a non-linear face reconstruction representation as a guidance of latent space to obtain more accurate deformation, which helps solve the geometry-related deformation and is good for generalization across subjects. Huber and HSIC (Hilbert-Schmidt Independence Criterion) constraints are adopted to promote the robustness of our model and to better exploit the non-linear and high-order correlations. Experimental results on the public dataset and real scanned dataset validate the superiority of our proposed GDPnet compared with state-of-the-art model. We will make the code available for research purposes.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	1077-2626
Date of First Compliant Deposit:	2 September 2021
Date of Acceptance:	17 August 2021
Last Modified:	24 Nov 2024 08:45
URI:	https://orca.cardiff.ac.uk/id/eprint/143861

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