Recent advances in neural models have shown excellent results for virtual fitting tasks (VTO), where a 3D representation of a garment is deformed to fit the target body shape. However, existing solutions are limited to a single layer of clothing and cannot solve the combinatorial complexity of mixing different types of clothing.
To address this limitation, scientists present non-overlapping layered neural fields, ULNeFs, which solve the multi object interaction problem using implicit object representations. They represent multiple possibly colliding objects (e.g., multiple items of clothing) using a layered version of the neural fields and develop an algorithm that unravels these layered neural fields to represent objects without collisions.
What happens is that they first learn two fields: a landmark distance field f(x), which represents the surface of a garment and gives the notion of "inside-outside," and a covariant field h(x), which models the volume near holes through which other garment objects can pass without creating entangled configurations. Using these fields, it is possible to determine whether point x is in a tangled configuration. And thanks to the implicit surface representation, untangling can be formulated as a local operation on the values of the fields at points x.
This state-of-the-art neural projection operator is trained only once for any arbitrary combination of N surfaces, and after training it naturally generalizes to any garment or implicit surface.ULNeFs can produce order-dependent results, depending on how the layers are sorted.
- Project - http://mslab.es/projects/ULNeF/
- Paper - http://mslab.es/projects/ULNeF/contents/santesteban_NeurIPS2022.pdf
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