We’re happy to report that our paper “ULNeF: Untangled Layered Neural Fields for Mix-and-Match Virtual Try-On” will be presented at NeurIPS next week. This approach solves the problem of multi-layer cloth collision with a learned untangling operator.
The project website is https://mslab.es/projects/ULNeF/ , and the corresponding video is highly recommended https://www.youtube.com/watch?v=elpg6LjC61k&feature=emb_logo.
Full abstract: Recent advances in neural models have shown great results for virtual try-on (VTO) problems, where a 3D representation of a garment is deformed to fit a target body shape. However, current solutions are limited to a single garment layer, and cannot address the combinatorial complexity of mixing different garments. Motivated by this limitation, we investigate the use of neural fields for mix-and-match VTO, and identify and solve a fundamental challenge that existing neural-field methods cannot address: the interaction between layered neural fields. To this end, we propose a neural model that untangles layered neural fields to represent collision-free garment surfaces. The key ingredient is a neural untangling projection operator that works directly on the layered neural fields, not on explicit surface representations. Algorithms to resolve object-object interaction are inherently limited by the use of explicit geometric representations, and we show how methods that work directly on neural implicit representations could bring a change of paradigm and open the door to radically different approaches.