Poly-GAN: Multi-Conditioned GAN for Fashion Synthesis

TL;DR

Poly-GAN is a versatile multi-conditioned GAN architecture designed for fashion synthesis tasks like garment placement, alignment, stitching, and inpainting, achieving state-of-the-art results on the DeepFashion dataset.

Contribution

It introduces a unified GAN architecture that performs multiple fashion synthesis tasks with conditions enforced at all layers, unlike previous multi-network pipelines.

Findings

Achieves state-of-the-art SSIM and Inception Score on DeepFashion.

Performs spatial garment transformation based on RGB skeletons.

Handles irregular holes in garment inpainting.

Abstract

We present Poly-GAN, a novel conditional GAN architecture that is motivated by Fashion Synthesis, an application where garments are automatically placed on images of human models at an arbitrary pose. Poly-GAN allows conditioning on multiple inputs and is suitable for many tasks, including image alignment, image stitching, and inpainting. Existing methods have a similar pipeline where three different networks are used to first align garments with the human pose, then perform stitching of the aligned garment and finally refine the results. Poly-GAN is the first instance where a common architecture is used to perform all three tasks. Our novel architecture enforces the conditions at all layers of the encoder and utilizes skip connections from the coarse layers of the encoder to the respective layers of the decoder. Poly-GAN is able to perform a spatial transformation of the garment based on the RGB skeleton of the model at an arbitrary pose. Additionally, Poly-GAN can perform image stitching, regardless of the garment orientation, and inpainting on the garment mask when it contains irregular holes. Our system achieves state-of-the-art quantitative results on Structural Similarity Index metric and Inception Score metric using the DeepFashion dataset.