PhysHead：可直接用于仿真的高斯头部化身
PhysHead: Simulation-Ready Gaussian Head Avatars
把 3D 高斯基元同时绑定在头部参数化网格与可物理仿真的发丝段上，为可动头部化身提供了一种「网格+发丝+高斯外观」的混合表示；其用 VLM 补全动态序列中被遮挡区域外观的训练策略，可迁移到其他存在遮挡缺失的多视角动态重建任务。
原文依据 we propose the use of VLM-based models to generate appearance of regions that are occluded in the dynamic training sequences
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论逼真的数字化身需要富有表现力且动态的头发运动；然而，现有大多数头部化身方法都假设头发为刚性运动。这些方法往往无法将头发与头部分离，而是将其表示为一层简单的外壳，因而无法刻画头发自然的体积行为。本文针对这些局限提出 PhysHead，一种可从多视角视频中学习真实头发动力学的、可动画化的头部化身混合表示。其核心是基于 3D 高斯的分层头部表示。我们的方法将头部的 3D 参数化网格与基于发丝的头发相结合，后者可直接使用物理引擎进行仿真。在外观模型方面，我们采用同时附着于头部网格与发丝段的各向同性高斯基元。该表示能够创建具有动态头发行为（例如风吹运动）的照片级真实感头部化身，从而克服现有方法中头发刚性的限制。然而，这些动画能力也要求新的训练方案。具体而言，我们提出使用基于 VLM 的模型来生成动态训练序列中被遮挡区域的外观。在定量与定性研究中，我们展示了所提模型的能力，并将其与现有基线进行比较。我们表明，除表情与相机控制之外，本方法还能合成物理上合理的头发运动。更多结果与代码请参见 https://physhead.github.io。
英文原文摘要 1513 字符
Realistic digital avatars require expressive and dynamic hair motion; however, most existing head avatar methods assume rigid hair movement. These methods often fail to disentangle hair from the head, representing it as a simple outer shell and failing to capture its natural volumetric behavior. In this paper, we address these limitations by introducing PhysHead, a hybrid representation for animatable head avatars with realistic hair dynamics learned from multiview video. At the core is a 3D Gaussian-based layered representation of the head. Our approach combines a 3D parametric mesh for the head with strand-based hair, which can be directly simulated using physics engines. For the appearance model, we employ Gaussian primitives attached to both the head mesh and hair segments. This representation enables the creation of photorealistic head avatars with dynamic hair behavior, such as wind-blown motion, overcoming the constraints of rigid hair in existing methods. However, these animation capabilities also require new training schemes. In particular, we propose the use of VLM-based models to generate appearance of regions that are occluded in the dynamic training sequences. In quantitative and qualitative studies, we demonstrate the capabilities of the proposed model and compare it with existing baselines. We show that our method can synthesize physically plausible hair motion besides expression and camera control. For additional results and code, please refer to https://physhead.github.io