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RGB cloth generation has been deeply studied in the related literature,however, 3D garment generation remains an open problem. In this paper, we builda conditional variational autoencoder for 3D garment generation and draping. Wepropose a pyramid network to add garment details progressively in a canonicalspace, i.e. unposing and unshaping the garments w.r.t. the body. We studyconditioning the network on surface normal UV maps, as an intermediaterepresentation, which is an easier problem to optimize than 3D coordinates. Ourresults on two public datasets, CLOTH3D and CAPE, show that our model isrobust, controllable in terms of detail generation by the use ofmulti-resolution pyramids, and achieves state-of-the-art results that canhighly generalize to unseen garments, poses, and shapes even when training withsmall amounts of data.

A Generative Multi-Resolution Pyramid and Normal-Conditioning 3D Cloth Draping