Example-Based Feature Painting on Textures
Andrei-Timotei Ardelean, Tim Weyrich

TL;DR
This paper presents a learning-based, unsupervised system for controlled editing and generation of textures with local blemishes, enabling realistic and interactive texture creation without manual annotations.
Contribution
It introduces a novel unsupervised approach for texture editing that clusters features and guides conditional generation, including diffusion-based editing and infinite texture synthesis.
Findings
Effective unsupervised anomaly detection for blemish features
Clustering of textural features into semantic groups
Interactive texture editing and infinite generation capabilities
Abstract
In this work, we propose a system that covers the complete workflow for achieving controlled authoring and editing of textures that present distinctive local characteristics. These include various effects that change the surface appearance of materials, such as stains, tears, holes, abrasions, discoloration, and more. Such alterations are ubiquitous in nature, and including them in the synthesis process is crucial for generating realistic textures. We introduce a novel approach for creating textures with such blemishes, adopting a learning-based approach that leverages unlabeled examples. Our approach does not require manual annotations by the user; instead, it detects the appearance-altering features through unsupervised anomaly detection. The various textural features are then automatically clustered into semantically coherent groups, which are used to guide the conditional generation…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Computer Graphics and Visualization Techniques · 3D Shape Modeling and Analysis
