  This work presents CLOTH3D, the first big scale synthetic dataset of 3D
clothed human sequences. CLOTH3D contains a large variability on garment type,
topology, shape, size, tightness and fabric. Clothes are simulated on top of
thousands of different pose sequences and body shapes, generating realistic
cloth dynamics. We provide the dataset with a generative model for cloth
generation. We propose a Conditional Variational Auto-Encoder (CVAE) based on
graph convolutions (GCVAE) to learn garment latent spaces. This allows for
realistic generation of 3D garments on top of SMPL model for any pose and
shape.
