用扩散模型实现单图真人发型自然动态，支持细粒度控制。
HairWeaver: Few-Shot Photorealistic Hair Motion Synthesis with Sim-to-Real Guided Video Diffusion
- 引入运动上下文与风格对齐双LoRA模块，精准引导发丝运动。
- 在模拟生成的动态人体数据上训练，实现发丝随动作自然飘动。
- 适合影视动画、虚拟人等需要高真实感发型动态的场景。
我们提出HairWeaver，一种基于扩散模型的流水线，可为单张人脸图像生成逼真且富有表现力的头发动态。现有方法虽能控制身体姿态，却缺乏对头发的精细控制，导致动画僵硬不自然。HairWeaver通过两个专用模块克服该问题：运动上下文LoRA（Motion-Context-LoRA）整合运动条件，风格对齐LoRA（Style-Alignment-LoRA）保持主体在不同数据域中的逼真外观。这两个轻量级组件在不破坏视频扩散主干核心生成能力的前提下，引导其生成高质量动画。模型在由计算机图形学模拟器生成的动态人体数据集上训练，实现对头发运动的精细调控，并最终学习到响应自然动作的高保真发丝动态。全面评估表明，该方法达到新基准，生成具有丰富动态细节的真实人类发型动画。
原文摘要 · Abstract (English)
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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