InspirNET: An Unsupervised Generative Adversarial Network with Controllable Fine-grained Texture Disentanglement for Fashion Generation

Texture constitutes the color and fabric of fashion items. Its choice in fashion items can directly express the personality and emotional state of a wearer. Despite the rapid development of intelligence-driven fashion design, it remains challenging to achieve independent control over texture without affecting other attributes, due to the highly intertwined nature of texture space. To accomplish fine-grained texture disentanglement, we propose InspirNET, an unsupervised disentangled generative adversarial framework, that manipulates textures in a fine-grained latent space so as to produce new textures effectively, aiming to broaden the range of fashion options available to common users with distinct textures as well as boosting designers' potential for fashion innovation and inspiration. Specifically, we first introduce an auto-fashion attribute encoder to map the input fashion item into texture and structure spaces. To achieve unsupervised fine-grained texture disentanglement, our model proposes a K-textures disentanglement module that decomposes the texture space into several orthogonal vectors, each of which is empowered to control an independent texture element. In particular, by employing an orthogonal eigenvector to interpolate with another, a multitude of new textures can be generated easily. Qualitative and quantitative experiments demonstrate that our InspirNET can effectively utilize decomposed orthogonal vectors to generate a wide range of fashion items with diverse textures. Our model exhibits superior performance over state-of-the-art methods in terms of maintaining the authenticity of texture transfer.