InterAnimate: Taming Region-aware Diffusion Model for Realistic Human Interaction Animation

• URL: http://arxiv.org/abs/2504.10905v1
• Date: Tue, 15 Apr 2025 06:32:45 GMT
• Title: InterAnimate: Taming Region-aware Diffusion Model for Realistic Human Interaction Animation
• Authors: Yukang Lin, Yan Hong, Zunnan Xu, Xindi Li, Chao Xu, Chuanbiao Song, Ronghui Li, Haoxing Chen, Jun Lan, Huijia Zhu, Weiqiang Wang, Jianfu Zhang, Xiu Li,
• Abstract summary: We introduce a novel motion paradigm for animating realistic hand-face interactions.<n>Our approach simultaneously learns anatomically-temporal contact dynamics and biomechanically plausible deformation effects.<n>Results show InterAnimate produces highly realistic animations, setting a new benchmark.
• Score: 47.103725372531784
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent video generation research has focused heavily on isolated actions, leaving interactive motions-such as hand-face interactions-largely unexamined. These interactions are essential for emerging biometric authentication systems, which rely on interactive motion-based anti-spoofing approaches. From a security perspective, there is a growing need for large-scale, high-quality interactive videos to train and strengthen authentication models. In this work, we introduce a novel paradigm for animating realistic hand-face interactions. Our approach simultaneously learns spatio-temporal contact dynamics and biomechanically plausible deformation effects, enabling natural interactions where hand movements induce anatomically accurate facial deformations while maintaining collision-free contact. To facilitate this research, we present InterHF, a large-scale hand-face interaction dataset featuring 18 interaction patterns and 90,000 annotated videos. Additionally, we propose InterAnimate, a region-aware diffusion model designed specifically for interaction animation. InterAnimate leverages learnable spatial and temporal latents to effectively capture dynamic interaction priors and integrates a region-aware interaction mechanism that injects these priors into the denoising process. To the best of our knowledge, this work represents the first large-scale effort to systematically study human hand-face interactions. Qualitative and quantitative results show InterAnimate produces highly realistic animations, setting a new benchmark. Code and data will be made public to advance research.

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