Title: ControlHair: Physically-based Video Diffusion for Controllable Dynamic Hair Rendering Show Chinese title

Title: ControlHair：可控制动态头发渲染的物理基础视频扩散

Abstract: Hair simulation and rendering are challenging due to complex strand dynamics, diverse material properties, and intricate light-hair interactions. Recent video diffusion models can generate high-quality videos, but they lack fine-grained control over hair dynamics. We present ControlHair, a hybrid framework that integrates a physics simulator with conditional video diffusion to enable controllable dynamic hair rendering. ControlHair adopts a three-stage pipeline: it first encodes physics parameters (e.g., hair stiffness, wind) into per-frame geometry using a simulator, then extracts per-frame control signals, and finally feeds control signals into a video diffusion model to generate videos with desired hair dynamics. This cascaded design decouples physics reasoning from video generation, supports diverse physics, and makes training the video diffusion model easy. Trained on a curated 10K video dataset, ControlHair outperforms text- and pose-conditioned baselines, delivering precisely controlled hair dynamics. We further demonstrate three use cases of ControlHair: dynamic hairstyle try-on, bullet-time effects, and cinemagraphic. ControlHair introduces the first physics-informed video diffusion framework for controllable dynamics. We provide a teaser video and experimental results on our website. Show Chinese abstract

Abstract: 头发模拟和渲染由于复杂的纤维动力学、多样的材料属性以及复杂的光线-头发相互作用而具有挑战性。 最近的视频扩散模型可以生成高质量的视频，但它们在头发动力学方面缺乏细粒度控制。 我们提出了ControlHair，这是一种混合框架，将物理模拟器与条件视频扩散模型相结合，以实现可控的动态头发渲染。 ControlHair采用三阶段流程：首先使用模拟器将物理参数（例如，头发硬度、风力）编码为每帧几何形状，然后提取每帧的控制信号，最后将控制信号输入视频扩散模型以生成具有所需头发动力学的视频。 这种级联设计将物理推理与视频生成解耦，支持多种物理效果，并使视频扩散模型的训练变得容易。 在整理好的10K视频数据集上进行训练， ControlHair优于文本和姿态条件基线，实现了精确控制的头发动力学。 我们进一步展示了ControlHair的三个应用场景：动态发型试戴、子弹时间效果和电影图形。 ControlHair引入了第一个基于物理信息的可控动力学视频扩散框架。 我们在网站上提供了演示视频和实验结果。