  In this paper, we propose StereoPIFu, which integrates the geometric
constraints of stereo vision with implicit function representation of PIFu, to
recover the 3D shape of the clothed human from a pair of low-cost rectified
images. First, we introduce the effective voxel-aligned features from a stereo
vision-based network to enable depth-aware reconstruction. Moreover, the novel
relative z-offset is employed to associate predicted high-fidelity human depth
and occupancy inference, which helps restore fine-level surface details.
Second, a network structure that fully utilizes the geometry information from
the stereo images is designed to improve the human body reconstruction quality.
Consequently, our StereoPIFu can naturally infer the human body's spatial
location in camera space and maintain the correct relative position of
different parts of the human body, which enables our method to capture human
performance. Compared with previous works, our StereoPIFu significantly
improves the robustness, completeness, and accuracy of the clothed human
reconstruction, which is demonstrated by extensive experimental results.
