Unsupervised Compositional Neural Representation for Texture Editing

This work introduces a fully unsupervised approach for representing textures using a compositional neural model that captures individual textons as a composition of 2D Gaussian functions with associated appearance features. This representation enables a wide range of texture editing applications.