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

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

arXiv · 2602.11117

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

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

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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 Style-Alignment-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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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. 2026-02-11. HairWeaver: Few-Shot Photorealistic Hair Motion Synthesis with Sim-to-Real Guided Video Diffusion. https://arxiv.org/abs/2602.11117

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