MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Images. (arXiv:2106.11944v2 [cs.CV] UPDATED)

In this paper, we aim to create generalizable and controllable neural signed
distance fields (SDFs) that represent clothed humans from monocular depth
observations. Recent advances in deep learning, especially neural implicit
representations, have enabled human shape reconstruction and controllable
avatar generation from different sensor inputs. However, to generate realistic
cloth deformations from novel input poses, watertight meshes or dense full-body
scans are usually needed as inputs. Furthermore, due to the difficulty of
effectively modeling pose-dependent cloth deformations for diverse body …

arxiv cv human images learning