Controllable Garment Image Synthesis Integrated with Frequency Domain Features

Abstract

Using sketches and textures to synthesize garment images is able to conveniently display the realistic visual effect in the design phase, which greatly increases the efficiency of fashion design. Existing garment image synthesis methods from a sketch and a texture tend to fail in working on complex textures, especially those with periodic patterns. We propose a controllable garment image synthesis framework that takes as inputs an outline sketch and a texture patch and generates garment images with complicated and diverse texture patterns. To improve the performance of global texture expansion, we exploit the frequency domain features in the generative process, which are from a Fast Fourier Transform (FFT) and able to represent the periodic information of the patterns. We also introduce a perceptual loss in the frequency domain to measure the similarity of two texture pattern patches in terms of their intrinsic periodicity and regularity. Comparisons with existing approaches and sufficient ablation studies demonstrate the effectiveness of our method that is capable of synthesizing impressive garment images with diverse texture patterns while guaranteeing proper texture expansion and pattern consistency.