Per Garment Capture and Synthesis for Real-time Virtual Try-on

TL;DR

This paper introduces a novel workflow for virtual try-on that captures detailed garment deformations under various poses using an actuated mannequin and deep image translation, enabling more realistic and interactive online garment fitting.

Contribution

It presents a new per garment capture and synthesis method using systematic image collection and deep learning to improve virtual try-on realism and interactivity.

Findings

Captured detailed garment deformations under diverse poses.

Achieved realistic garment synthesis through deep image translation.

Enabled interactive online garment fitting.

Abstract

Virtual try-on is a promising application of computer graphics and human computer interaction that can have a profound real-world impact especially during this pandemic. Existing image-based works try to synthesize a try-on image from a single image of a target garment, but it inherently limits the ability to react to possible interactions. It is difficult to reproduce the change of wrinkles caused by pose and body size change, as well as pulling and stretching of the garment by hand. In this paper, we propose an alternative per garment capture and synthesis workflow to handle such rich interactions by training the model with many systematically captured images. Our workflow is composed of two parts: garment capturing and clothed person image synthesis. We designed an actuated mannequin and an efficient capturing process that collects the detailed deformations of the target garments under diverse body sizes and poses. Furthermore, we proposed to use a custom-designed measurement garment, and we captured paired images of the measurement garment and the target garments. We then learn a mapping between the measurement garment and the target garments using deep image-to-image translation. The customer can then try on the target garments interactively during online shopping.