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

摘要

We present HairWeaver, a diffusion-based pipeline that ani-mates a single human image with realistic and expressive hair dynamics.While existing methods successfully control body pose, they lack specificcontrol over hair, and as a result, fail to capture the intricate hair mo-tions, resulting in stiff and unrealistic animations. HairWeaver overcomesthis limitation using two specialized modules: a Motion-Context-LoRAto integrate motion conditions and a Style-Alignment-LoRA to pre-serve the subject’s photoreal appearance across different data domains.These lightweight components are designed to guide a video diffusionbackbone while maintaining its core generative capabilities. By trainingon a specialized dataset of dynamic human motion generated from aCG simulator, HairWeaver affords fine control over hair motion and ulti-mately learns to produce highly realistic hair that responds naturally tomovement. Comprehensive evaluations demonstrate that our approachsets a new state of the art, producing lifelike human hair animationswith dynamic details.