Personalized Facial Expressions and Head Poses for Speech-Driven 3D Facial Animation

Liyang Chen, Weihong Bao, Shun Lei, Boshi Tang, Zhiyong Wu, Shiyin Kang, Haozhi Huang

Keywords: 3D Facial Animation, Low-Rank Adaptation, Head Pose

TL;DR: Speech-Driven 3D Facial Animation with Personalized Facial Expressions and Head Poses

Abstract: Speech-driven 3D facial animation aims at generating facial movements that are synchronized with the driving speech, which has been widely explored recently. Existing works mostly neglect the person-specific talking style in generation, including facial expression and head pose styles. Several works intend to capture the personalities by fine-tuning modules. However, limited training data leads to the lack of vividness. In this work, we propose AdaMesh, a novel adaptive speech-driven facial animation approach, which learns the personalized talking style from a reference video of about 10 seconds and generates vivid facial expressions and head poses. Specifically, we propose mixture-of-low-rank adaptation (MoLoRA) to fine-tune the expression adapter, which efficiently captures the facial expression style. For the personalized pose style, we propose a pose adapter by building a discrete pose prior and retrieving the appropriate style embedding with a semantic-aware pose style matrix without fine-tuning. Extensive experimental results show that our approach outperforms state-of-the-art methods, preserves the talking style in the reference video, and generates vivid facial animation. The supplementary video and code will be available at https://adamesh.github.io.

Primary Area: representation learning for computer vision, audio, language, and other modalities

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Submission Number: 7094