Learning to Dress 3D People in Generative Clothing

a) (b) (c) (d) (e) (f) Figure 1: CAPE model for clothed humans. Given a SMPL body shape and pose (a), CAPE adds clothing by randomly sampling from a learned model (b, c), can generate different clothing types -shorts in (b, c) vs. long-pants in (d). The generated clothed humans can generalize to diverse body shapes (e) and body poses (f). Best viewed zoomed-in on screen.