Learning to Disentangle the Colors, Textures, and Shapes of Fashion Items: A Unified Framework.

Today, fashion design can be readily performed by most people due to the rapid development of design tools. However, not everyone possesses the professional skills to produce an aesthetically pleasing design. In order to assist an inexperienced user during the design process, this research explored a new fashion-related disentanglement task, with the goal of creating novel fashion items with controllable attributes. The key idea is to develop a unified framework, called CTS-GAN, by disentangling the colors, textures, and shapes of fashion items simultaneously using a generative adversarial network (GAN). Specifically, we first introduced a fashion attribute encoder to decompose input fashion items into three latent spaces, i.e., color, texture, and shape. A fashion item pattern-making module (FIPM)-based generator was then proposed to control the corresponding parameters of color and texture in FIPMs independently and combine them with the shape features in order to accomplish the final generation of new fashion items. Furthermore, three independent pathways were introduced to extract the representations of color, texture, and shape in fashion items to optimize our CTS-GAN in an unsupervised manner. Extensive experimental results demonstrate the effectiveness of our CTS-GAN and suggest that it can generate diverse, novel fashion images by taking full advantage of the controllability of the colors, textures, and shapes of different fashion items