用物理模拟实现衣服随动作自然变形，重建更真实的穿衣虚拟人。
PBDyG: Position Based Dynamic Gaussians for Motion-Aware Clothed Human Avatars
- 基于位置的动态高斯模型，让衣服随身体运动真实摆动。
- 可精准重建裙子、外套等易变形衣物，效果优于现有方法。
- 适合做虚拟试衣、动画生成或数字人交互的科研与工程人员。
本文提出一种新型穿衣人体模型，可通过多视角RGB视频学习，重点恢复身体与衣物的真实运动。所提方法Position Based Dynamic Gaussians（PBDyG）通过物理仿真实现“运动相关”的衣物形变，而非仅依赖“姿态相关”的刚性变换。整体建模采用3D高斯表示衣物，并附着于遵循输入视频动作的皮肤化SMPL人体模型。SMPL人体的运动驱动衣物高斯的物理仿真，实现新姿态下的逼真变形。为支持位置基动力学仿真，物理属性如质量与材料刚度由动态3D高斯点云从RGB视频中估计得出。实验表明，该方法不仅能准确还原外观，还能成功重建裙装、大衣等高度可变形服装，解决了现有方法难以处理此类衣物的难题。
原文摘要 · Abstract (English)
This paper introduces a novel clothed human model that can be learned from multiview RGB videos, with a particular emphasis on recovering physically accurate body and cloth movements. Our method, Position Based Dynamic Gaussians (PBDyG), realizes ``movement-dependent'' cloth deformation via physical simulation, rather than merely relying on ``pose-dependent'' rigid transformations. We model the clothed human holistically but with two distinct physical entities in contact: clothing modeled as 3D Gaussians, which are attached to a skinned SMPL body that follows the movement of the person in the input videos. The articulation of the SMPL body also drives physically-based simulation of the clothes' Gaussians to transform the avatar to novel poses. In order to run position based dynamics simulation, physical properties including mass and material stiffness are estimated from the RGB videos through Dynamic 3D Gaussian Splatting. Experiments demonstrate that our method not only accurately reproduces appearance but also enables the reconstruction of avatars wearing highly deformable garments, such as skirts or coats, which have been challenging to reconstruct using existing methods.
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