Multi-View High Precise 3D Human Body Reconstruction Method for Virtual Fitting

Abstract

Online shopping has experienced rapid development recently. However, compared to offline shopping, the return rate and complaint rate of online shopping are much higher, especially for online clothes shopping. In order to solve this problem, virtual fitting technology arises at the right moment, and 3D human modeling is a crucial part of virtual fitting technology. The reconstruction of the parametric 3D human models often faces challenges as long fitting time, low accuracy and fuzzy depth information. Although the reconstruction of nonparametric 3D human model has improved accuracy and detail to some extent, such models typically lack flexibility and controllability. Therefore, this paper reconstructs a high-precision parametric 3D human model by proposing a multi-view iterative registration strategy based on pose prior estimation, which is integrated with a nonparametric 3D human model based on implicit functions. The resulting model retains not only high-precision and detail but also high flexibility and controllability, which has achieved a good effect on the application of 3D virtual fitting. The proposed method is tested on the open-source 3D human body dataset multi-garment network (MGN). The Chamfer distance of the SMPL mannequin reconstructed from multiple views can reach 2.18[Formula: see text]cm and the per-vertical-error can reach 1.63[Formula: see text]cm.