HairWeaver: Few-Shot Photorealistic Hair Motion Synthesis with Sim-to-Real Guided Video Diffusion

• URL: http://arxiv.org/abs/2602.11117v1
• Date: Wed, 11 Feb 2026 18:31:47 GMT
• Title: HairWeaver: Few-Shot Photorealistic Hair Motion Synthesis with Sim-to-Real Guided Video Diffusion
• Authors: Di Chang, Ji Hou, Aljaz Bozic, Assaf Neuberger, Felix Juefei-Xu, Olivier Maury, Gene Wei-Chin Lin, Tuur Stuyck, Doug Roble, Mohammad Soleymani, Stephane Grabli,
• Abstract summary: HairWeaver animates a single human image with realistic and expressive hair dynamics.<n>Our approach sets a new state of the art, producing lifelike human hair animations with dynamic details.
• Score: 28.955744141374993
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present HairWeaver, a diffusion-based pipeline that animates a single human image with realistic and expressive hair dynamics. While existing methods successfully control body pose, they lack specific control over hair, and as a result, fail to capture the intricate hair motions, resulting in stiff and unrealistic animations. HairWeaver overcomes this limitation using two specialized modules: a Motion-Context-LoRA to integrate motion conditions and a Sim2Real-Domain-LoRA to preserve the subject's photoreal appearance across different data domains. These lightweight components are designed to guide a video diffusion backbone while maintaining its core generative capabilities. By training on a specialized dataset of dynamic human motion generated from a CG simulator, HairWeaver affords fine control over hair motion and ultimately learns to produce highly realistic hair that responds naturally to movement. Comprehensive evaluations demonstrate that our approach sets a new state of the art, producing lifelike human hair animations with dynamic details.

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