  It is very challenging to accurately reconstruct sophisticated human geometry
caused by various poses and garments from a single image. Recently, works based
on pixel-aligned implicit function (PIFu) have made a big step and achieved
state-of-the-art fidelity on image-based 3D human digitization. However, the
training of PIFu relies heavily on expensive and limited 3D ground truth data
(i.e. synthetic data), thus hindering its generalization to more diverse real
world images. In this work, we propose an end-to-end self-supervised network
named SelfPIFu to utilize abundant and diverse in-the-wild images, resulting in
largely improved reconstructions when tested on unconstrained in-the-wild
images. At the core of SelfPIFu is the depth-guided volume-/surface-aware
signed distance fields (SDF) learning, which enables self-supervised learning
of a PIFu without access to GT mesh. The whole framework consists of a normal
estimator, a depth estimator, and a SDF-based PIFu and better utilizes extra
depth GT during training. Extensive experiments demonstrate the effectiveness
of our self-supervised framework and the superiority of using depth as input.
On synthetic data, our Intersection-Over-Union (IoU) achieves to 93.5%, 18%
higher compared with PIFuHD. For in-the-wild images, we conduct user studies on
the reconstructed results, the selection rate of our results is over 68%
compared with other state-of-the-art methods.
