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

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.

Resource

Paper
Author's draft (Google Drive)
arXiv

Talk Video
10 minutes talk for UIST2021 presentation (YouTube)

Training Data
We provide data captured using our system. Please cite our paper if you use the data.
Each zip file includes all RGB images associated with a garment along with a binary mask of the garment.
Please find the data here (Google Drive).

Measurement garment
If you would like to purchase the measurement garment, you can view the design and directly purchase from this link.
Note that we are not associated with the manufacturer and are not responsible for your purchase.

Coverage

Citation