  Although much progress has been made in 3D clothed human reconstruction, most
of the existing methods fail to produce robust results from in-the-wild images,
which contain diverse human poses and appearances. This is mainly due to the
large domain gap between training datasets and in-the-wild datasets. The
training datasets are usually synthetic ones, which contain rendered images
from GT 3D scans. However, such datasets contain simple human poses and less
natural image appearances compared to those of real in-the-wild datasets, which
makes generalization of it to in-the-wild images extremely challenging. To
resolve this issue, in this work, we propose ClothWild, a 3D clothed human
reconstruction framework that firstly addresses the robustness on in-thewild
images. First, for the robustness to the domain gap, we propose a weakly
supervised pipeline that is trainable with 2D supervision targets of
in-the-wild datasets. Second, we design a DensePose-based loss function to
reduce ambiguities of the weak supervision. Extensive empirical tests on
several public in-the-wild datasets demonstrate that our proposed ClothWild
produces much more accurate and robust results than the state-of-the-art
methods. The codes are available in here:
https://github.com/hygenie1228/ClothWild_RELEASE.

Computer Vision and Pattern Recognition