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ULNeF: Untangled Layered Neural Fields for Mix-and-Match Virtual Try-On
Igor Santesteban · Miguel Otaduy · Nils Thuerey · Dan Casas

Tue Nov 29 02:00 PM -- 04:00 PM (PST) @ Hall J #431

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.

Author Information

Igor Santesteban (Universidad Rey Juan Carlos)
Miguel Otaduy (Universidad Rey Juan Carlos)
Nils Thuerey (Technical University of Munich)
Dan Casas (Universidad Rey Juan Carlos)

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