S${}^2$Human: Bridging SMPL-X and Stereo Matching for Pose-Free 3D Human Reconstruction

Recent years have seen rapid development in 3D human reconstruction, a technology that produces digital human models based on image or video inputs. However, existing methods heavily rely on pre-calibrated camera parameters, thus limiting the applicability in consumer-level scenarios. While intrinsic camera parameters can be readily obtained from device specifications, the pose-free 3D human reconstruction framework remains crucial. In this paper, we propose a pose-free 3D human reconstruction framework named S${}^2$Human by bridging SMPL-X and Stereo matching. Stereo matching delivers pose-free capability via extensive 3D reconstruction priors, while SMPL-X supplies human priors to guide the enhancement of geometric accuracy and fine-grained texture details. Specifically, S${}^2$Human presents two key innovations. On the one hand, a SMPL-X Estimation module is proposed to acquire a unified SMPL-X model from SMPL-X monocular estimations of different views, providing a robust and consistent human prior. On the other hand, a Posemap Adapter module is designed to inject structured human body information from the unified SMPL-X model into the stereo matching network, significantly enhancing both geometric accuracy and textural details in the reconstructed human model. Experimental results on the THuman2.0 and Twindom datasets demonstrate that S${}^2$Human is competitive with the pose-aware methods.