REALISTIC MONOCULAR-TO-3D VIRTUAL TRY-ON VIA MULTI-SCALE CHARACTERISTICS CAPTURE

3D virtual try-on receives widespread attention from scholars due to its great practical and commercial values. In prior methods, the fundamental problems lie in the limitations on texture retention during garment deformation and the lack of feature context capture during depth estimation. To address these problems, we propose a new 3D virtual try-on network via multi-scale characteristic capture (VTON-MC), which can produce an exact 3D model with the generated photo-realistic monocular image. The main processes are as follows: 1) predicting the human semantic-map and aligning the in-shop garment in the human pose using the appearance flow method, 2) synthesizing the human body and the warped garment to gain the image try-on result, and 3) estimating the human double-depth map of the image try-on result to reconstruct desired 3D try-on mesh by designed Depth Estimation Network (DEN). Extensive experiments on existing benchmark datasets demonstrate that VTON-MC outperforms state-of-the-art approaches efficiently.