CLOTH3D: The fitting rooms of the future
Nowadays, it exits models for simulating clothes on top of body shapes, but they are almost focused on 2D. This is because 3D models need an enormous amount of data, and available 3D cloth data are very scarce. There are three main strategies in order to produce data of 3D dressed humans: 3D scans, 3D from conventional images, and synthetic generation. In the case of 3D scans, they are costly, and at most they can produce a single mesh (human + garments). Alternatively, datasets that infer 3D geometry of clothes from conventional images are inaccurate and cannot properly model cloth dynamics. Finally, synthetic data is easy to generate and is ground truth error free. Following this last path CLOTH3D, the first big the first 3D big scale synthetic dataset for simulating clothes on top of different body shapes, was developed.
CLOTH3D is the first 3D big scale synthetic dataset for simulating clothes on top of different body shapes. It 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. CLOTH3D is unique in terms of garment, shape, and pose variability, including more than 2 million 3D samples.
This dataset is the first step to allow virtual enhanced try-ons experience, reducing designers and animator’s workload.