Example-Based Feature Painting on Textures

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 of images. Our pipeline as a whole goes from a small image collection to a versatile generative model that enables the user to interactively create and paint features on textures of arbitrary size. Notably, the algorithms we introduce for diffusion-based editing and infinite stationary texture generation are generic and should prove useful in other contexts as well.

Citation Style: Display Alpha ACM SIGGRAPH IEEEtran Publication

Example-Based Feature Painting on Textures.
Andrei-Timotei Ardelean, Tim Weyrich.
ACM Transactions on Graphics (Proc. SIGGRAPH Asia), 10 pages, 2025.Andrei-Timotei Ardelean and Tim Weyrich. Example-based feature painting on textures. ACM Transactions on Graphics (Proc. SIGGRAPH Asia), December 2025.Ardelean, A.-T., and Weyrich, T. 2025. Example-based feature painting on textures. ACM Transactions on Graphics (Proc. SIGGRAPH Asia) (Dec.).A.-T. Ardelean and T. Weyrich, “Example-based feature painting on textures,”ACM Transactions on Graphics (Proc. SIGGRAPH Asia), Dec. 2025.

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Acknowledgments

This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 956585.