A Method for Personalized Speech-Driven 3D Facial Animation Generation

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A Method for Personalized Speech-Driven 3D Facial Animation Generation

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[1]LI Wei,SONG Yupu,LIU Yazhi,et al.A Method for Personalized Speech-Driven 3D Facial Animation Generation[J].Journal of Zhengzhou University (Engineering Science),2027,48(XX):1-8.[doi:10.13705/j.issn.1671-6833.2026.04.023]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 48 Number of periods: 2027 XX Page number: 1-8 Column: Public date: 2027-12-10

Title:: A Method for Personalized Speech-Driven 3D Facial Animation Generation

Author(s):: LI Wei^1,2, SONG Yupu^1,2, LIU Yazhi^1,2, AN Yi^1,2; (1. College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China; 2. Hebei Key Laboratory of Industrial Intelligent Perception, North China University of Science and Technology, Tangshan 063210, China)

Keywords:: speech-driven animation; 3D facial animation; deep learning; diffusion model; personalization

CLC:: TP391.41 TN912.3

DOI:: 10.13705/j.issn.1671-6833.2026.04.023

Abstract:: To address the challenges of speech-driven 3D facial animation, including difficult alignment between speech and motion, loss of identity features, and limited personalized dynamic expression, a conditional diffusionbased generation framework was proposed. The framework used a dual-path style encoding structure to extract hierarchical identity features and dynamic motion features, and then applied a bidirectional attention mechanism to deeply fuse speech features with noisy motion features. Based on this design, an improved Transformer decoderguided by style conditions was introduced to generate high-quality motion sequences. Experiments on the BIWI, VOCASET, and 3DMEAD datasets showed that the proposed method achieved the best results in average vertex error (MVE) , lipvertex error (LVE) , and facial dynamic deviation (FDD) . Compared with the best baseline method on each metric, MVE, LVE, and FDD were reduced by 4.8%, 15.4%, and 13.4% respectively on BIWI, LVE was reduced by 14.9% on VOCASET, and MVE and FDD were reduced by 10.2% and 13.7% respectively on 3DMEAD. Subjective evaluation results further confirmed its advantages in visual naturalness and realism. The proposed method provided a new technical approach for high-fidelity generation, identity preservation, and personalized modeling of 3D facial animation.

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Last Update: 2026-05-15