  Dressed people reconstruction from images is a popular task with promising
applications in the creative media and game industry. However, most existing
methods reconstruct the human body and garments as a whole with the supervision
of 3D models, which hinders the downstream interaction tasks and requires
hard-to-obtain data. To address these issues, we propose an unsupervised
separated 3D garments and human reconstruction model (USR), which reconstructs
the human body and authentic textured clothes in layers without 3D models. More
specifically, our method proposes a generalized surface-aware neural radiance
field to learn the mapping between sparse multi-view images and geometries of
the dressed people. Based on the full geometry, we introduce a Semantic and
Confidence Guided Separation strategy (SCGS) to detect, segment, and
reconstruct the clothes layer, leveraging the consistency between 2D semantic
and 3D geometry. Moreover, we propose a Geometry Fine-tune Module to smooth
edges. Extensive experiments on our dataset show that comparing with
state-of-the-art methods, USR achieves improvements on both geometry and
appearance reconstruction while supporting generalizing to unseen people in
real time. Besides, we also introduce SMPL-D model to show the benefit of the
separated modeling of clothes and the human body that allows swapping clothes
and virtual try-on.
