Dress-Me-Up: A Dataset & Method for Self-Supervised 3D Garment
  Retargeting
Shanthika Naik Kunwar Singh Astitva Srivastava Dhawal Sirikonda Amit Raj Varun Jampani Avinash Sharma
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.