Compositional Neural Textures
Peihan Tu, Li-Yi Wei, Matthias Zwicker

TL;DR
This paper presents an unsupervised neural model that represents textures as compositions of Gaussian textons, enabling easy editing, synthesis, and transfer of textures in images.
Contribution
It introduces a novel compositional neural texture model using Gaussian textons for unsupervised texture representation and editing.
Findings
Allows intuitive texture editing by modifying Gaussian components
Enables efficient texture synthesis through a feed-forward generator
Supports diverse applications like texture transfer and animation
Abstract
Texture plays a vital role in enhancing visual richness in both real photographs and computer-generated imagery. However, the process of editing textures often involves laborious and repetitive manual adjustments of textons, which are the recurring local patterns that characterize textures. This work introduces a fully unsupervised approach for representing textures using a compositional neural model that captures individual textons. We represent each texton as a 2D Gaussian function whose spatial support approximates its shape, and an associated feature that encodes its detailed appearance. By modeling a texture as a discrete composition of Gaussian textons, the representation offers both expressiveness and ease of editing. Textures can be edited by modifying the compositional Gaussians within the latent space, and new textures can be efficiently synthesized by feeding the modified…
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Taxonomy
TopicsEvolution and Paleontology Studies
