Template-Free Try-on Image Synthesis via Semantic-guided Optimization

TL;DR

This paper introduces a template-free method for virtual try-on image synthesis that generates realistic images with target clothing and pose without requiring user-specified poses, addressing previous limitations.

Contribution

The proposed TF-TIS network synthesizes human try-on images without templates, effectively handling facial details, wrinkles, and occlusions, and improves over state-of-the-art methods.

Findings

Outperforms existing methods in qualitative and quantitative evaluations.

Effectively handles challenging cases like facial details and occlusions.

Does not require user-specified target poses.

Abstract

The virtual try-on task is so attractive that it has drawn considerable attention in the field of computer vision. However, presenting the three-dimensional (3D) physical characteristic (e.g., pleat and shadow) based on a 2D image is very challenging. Although there have been several previous studies on 2D-based virtual try-on work, most 1) required user-specified target poses that are not user-friendly and may not be the best for the target clothing, and 2) failed to address some problematic cases, including facial details, clothing wrinkles and body occlusions. To address these two challenges, in this paper, we propose an innovative template-free try-on image synthesis (TF-TIS) network. The TF-TIS first synthesizes the target pose according to the user-specified in-shop clothing. Afterward, given an in-shop clothing image, a user image, and a synthesized pose, we propose a novel model for synthesizing a human try-on image with the target clothing in the best fitting pose. The qualitative and quantitative experiments both indicate that the proposed TF-TIS outperforms the state-of-the-art methods, especially for difficult cases.