We propose a novel self-supervised framework for retargeting
non-parameterized 3D garments onto 3D human avatars of arbitrary shapes and
poses, enabling 3D virtual try-on (VTON). Existing self-supervised 3D
retargeting methods only support parametric and canonical garments, which can
only be draped over parametric body, e.g. SMPL. To facilitate the
non-parametric garments and body, we propose a novel method that introduces
Isomap Embedding based correspondences matching between the garment and the
human body to get a coarse alignment between the two meshes. We perform neural
refinement of the coarse alignment in a self-supervised setting. Further, we
leverage a Laplacian detail integration method for preserving the inherent
details of the input garment. For evaluating our 3D non-parametric garment
retargeting framework, we propose a dataset of 255 real-world garments with
realistic noise and topological deformations. The dataset contains $44$ unique
garments worn by 15 different subjects in 5 distinctive poses, captured using a
multi-view RGBD capture setup. We show superior retargeting quality on
non-parametric garments and human avatars over existing state-of-the-art
methods, acting as the first-ever baseline on the proposed dataset for
non-parametric 3D garment retargeting.

我们提出了一种新颖的自监督框架，用于在任意形状和姿势的三维人体化身上重定向非参数化的三维服装，实现三维虚拟试穿。我们引入了基于 Isomap 嵌入的对应匹配方法，以获得两个网格之间的粗略对齐，并在自监督环境下进行精细对齐的神经改进。此外，我们利用 Laplacian 细节整合方法来保留输入服装的内在细节，通过使用包含 255 种真实噪声和拓扑变形的真实世界服装数据集来评估我们的三维非参数化服装重定向框架，并展示了其在非参数化服装和人体化身上优于现有最先进方法的重定向质量，在该数据集上作为非参数化三维服装重定向的首个基准。