Controllable Expressive 3D Facial Animation via Diffusion in a Unified Multimodal Space

• URL: http://arxiv.org/abs/2506.10007v1
• Date: Mon, 14 Apr 2025 01:38:01 GMT
• Title: Controllable Expressive 3D Facial Animation via Diffusion in a Unified Multimodal Space
• Authors: Kangwei Liu, Junwu Liu, Xiaowei Yi, Jinlin Guo, Yun Cao,
• Abstract summary: We present a diffusion-based framework for controllable expressive 3D facial animation.<n>Our approach introduces two key innovations: (1) a FLAME-centered multimodal emotion binding strategy, and (2) an attention-based latent diffusion model.<n>Our method achieves a 21.6% improvement in emotion similarity while preserving physiologically plausible facial dynamics.
• Score: 7.165879904419689
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Audio-driven emotional 3D facial animation encounters two significant challenges: (1) reliance on single-modal control signals (videos, text, or emotion labels) without leveraging their complementary strengths for comprehensive emotion manipulation, and (2) deterministic regression-based mapping that constrains the stochastic nature of emotional expressions and non-verbal behaviors, limiting the expressiveness of synthesized animations. To address these challenges, we present a diffusion-based framework for controllable expressive 3D facial animation. Our approach introduces two key innovations: (1) a FLAME-centered multimodal emotion binding strategy that aligns diverse modalities (text, audio, and emotion labels) through contrastive learning, enabling flexible emotion control from multiple signal sources, and (2) an attention-based latent diffusion model with content-aware attention and emotion-guided layers, which enriches motion diversity while maintaining temporal coherence and natural facial dynamics. Extensive experiments demonstrate that our method outperforms existing approaches across most metrics, achieving a 21.6\% improvement in emotion similarity while preserving physiologically plausible facial dynamics. Project Page: https://kangweiiliu.github.io/Control_3D_Animation.

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