Clothing Visual Representation for 3D Human Reconstruction

Abstract

To address the problem of fuzzy representation of clothing visual information in three-dimensional human reconstruction, a multi-stage based optimization method is proposed. Firstly, semantic features, shading features and high-frequency features are extracted from input images in preprocess. Secondly, the signed distance field is constructed based on the local depth features to implicitly represent the shape of clothed human. Then, to optimize the signed distance field and generate the rough three-dimensional human model, the dress hierarchy module is constructed, and the clothing semantic context is perceived by defining the dress hierarchy loss function. Finally, combining the shading features and high-frequency features to capture the details of wrinkles in the UV space, and an accurate three-dimensional human model is generated. The experimental results on BUFF dataset show that the normal <italic>L</italic>₂ error and the chamfer distance error are 0.13 and 1.30 respectively, which reduce by 13% and 2%. The proposed method can improve the accuracy of three-dimensional human reconstruction, which generates three-dimensional human model with clothing style, dress hierarchy and wrinkles.