Recent progress in the field of 3D human shape estimation enables the efficient and accurate modeling of naked body shapes, but doesn't do so well when tasked with displaying the geometry of clothes. A team of researchers from Institut de Robòtica i Informàtica Industrial and Harvard University recently introduced 3DPeople, a large-scale comprehensive dataset with specific geometric shapes of clothes that is suitable for many computer vision tasks involving clothed humans. In addition to the new dataset, researchers also developed a novel shape parameterization algorithm and a multi-resolution end-to-end deep generative network for predicting dressed body shape. Researchers used four cameras to capture each subject's action sequence. In addition to providing textured 3D meshes for the clothing and bodies, researchers also annotated the dataset with RGB (one of the most widely used color systems, including almost all colors that can be perceived by human vision), 3D skeleton, depth, optical flow, and semantic information (body parts and cloth labels).