  RGB cloth generation has been deeply studied in the related literature,
however, 3D garment generation remains an open problem. In this paper, we build
a conditional variational autoencoder for 3D garment generation and draping. We
propose a pyramid network to add garment details progressively in a canonical
space, i.e. unposing and unshaping the garments w.r.t. the body. We study
conditioning the network on surface normal UV maps, as an intermediate
representation, which is an easier problem to optimize than 3D coordinates. Our
results on two public datasets, CLOTH3D and CAPE, show that our model is
robust, controllable in terms of detail generation by the use of
multi-resolution pyramids, and achieves state-of-the-art results that can
highly generalize to unseen garments, poses, and shapes even when training with
small amounts of data.
