Towards Universal Texture Synthesis by Combining Texton Broadcasting with Noise Injection in StyleGAN-2
This work addresses texture synthesis for computer vision applications, but it is incremental as it builds on the existing StyleGAN-2 framework.
The paper tackled the problem of universal texture synthesis by incorporating a multi-scale texton broadcasting module into StyleGAN-2, resulting in significantly better quality textures than state-of-the-art methods.
We present a new approach for universal texture synthesis by incorporating a multi-scale texton broadcasting module in the StyleGAN-2 framework. The texton broadcasting module introduces an inductive bias, enabling generation of broader range of textures, from those with regular structures to completely stochastic ones. To train and evaluate the proposed approach, we construct a comprehensive high-resolution dataset that captures the diversity of natural textures as well as stochastic variations within each perceptually uniform texture. Experimental results demonstrate that the proposed approach yields significantly better quality textures than the state of the art. The ultimate goal of this work is a comprehensive understanding of texture space.