FashionGAN: Display your fashion design using Conditional Generative Adversarial Nets

TLDR

Virtual garment display enables designers to view design effects without producing physical samples, a key advantage in fashion design. The paper proposes an end‑to‑end virtual garment display method using Conditional Generative Adversarial Networks. The method requires only a fashion sketch and a fabric image, then automatically generates a garment image with matching shape and texture, and can also handle contour and garment images to increase design reuse. Our method yields higher‑quality garment images with improved color and shape compared to existing image‑to‑image approaches.

Abstract

Abstract Virtual garment display plays an important role in fashion design for it can directly show the design effect of the garment without having to make a sample garment like traditional clothing industry. In this paper, we propose an end‐to‐end virtual garment display method based on Conditional Generative Adversarial Networks. Different from existing 3D virtual garment methods which need complex interactions and domain‐specific user knowledge, our method only need users to input a desired fashion sketch and a specified fabric image then the image of the virtual garment whose shape and texture are consistent with the input fashion sketch and fabric image can be shown out quickly and automatically. Moreover, it can also be extended to contour images and garment images, which further improves the reuse rate of fashion design. Compared with the existing image‐to‐image methods, the quality of images generated by our method is better in terms of color and shape.