用动态头发建模让头像更逼真，支持可控发型动画。
Head Avatars with Dynamic Explicit Hair
- 通过时序网络结合头部运动与重力，建模发丝动态变形。
- 在视频数据上实现高保真头发追踪与时间一致性表现。
- 适合虚拟人、影视特效等需要真实头发动画的场景。
我们提出DynHair，一种用于人类头部全息像动态头发追踪与建模的新方法。从视频输入出发，利用结构化3D高斯泼溅（3DGS）重建具有显式分丝级表示的动态头部全息像。与可基于表达性3D头模建模的面部区域不同，头发因呈现复杂动态运动而更具挑战性。为此，我们设计了一种新方法：通过条件于头部角速度、加速度及相对重力的时序网络，建模发丝动态形变。具体地，采用LSTM编码运动历史，并通过FiLM条件调制每点发丝特征，再由MLP生成符合物理规律的形变位移，以恢复初始发型。我们联合优化头发的运动与外观表示，与基于3DGS的面部区域表示，通过可微高斯泼溅实现，包含光度、几何与物理监督。实验表明，该方法在头发动态、时间一致性与跨主体泛化方面达到当前最优性能。
原文摘要 · Abstract (English)
We present DynHair, a novel method for tracking and modeling dynamic hair for human head avatars. From video input, we reconstruct a dynamic head avatar with an explicit strand-based hair representation using structured 3D Gaussian Splatting. In contrast to the face region of human head avatars, which can be modeled with 3D Gaussians that are attached or generated with respect to some expressive 3D head model, hair is particularly challenging as it exhibits dynamic motion effects. Therefore, we present a novel method that models the dynamic deformations of the hair strands using a temporal network that is conditioned on angular velocity and acceleration of the head, as well as relative gravity. Specifically, an LSTM encodes the motion history and modulates per-point strand features via FiLM conditioning which further used by MLP to produce physically plausible displacements to canonical hairstyle. We jointly optimize this motion and appearance representation of the hair, with a 3DGS-based representation of the face-region, via differentiable Gaussian splatting with photometric, geometric, and physics-based supervision. As a result of our method, we retrieve hair tracking of the training video data and an animatable head avatar with controllable hair dynamics. In our experiments, we demonstrate state-of-the-art performance in terms of hair dynamics, temporal consistency, and generalization across subjects.
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