用无标注样本自动生成带瑕疵的逼真纹理，支持交互式编辑。
Example-Based Feature Painting on Textures
- 基于无监督异常检测自动识别纹理瑕疵特征
- 通过聚类生成语义一致的特征组，实现条件化图像生成
- 支持任意尺寸纹理的无限生成，适合影视与游戏场景
本文提出一套完整的工作流程，实现具有显著局部特征的纹理的可控创作与编辑，包括污渍、撕裂、孔洞、磨损、变色等表面变化效果。这些变化在自然界中普遍存在，将其纳入合成过程对生成真实感纹理至关重要。我们提出一种基于学习的新方法，利用未标注样本创建带有此类缺陷的纹理，无需用户手动标注；通过无监督异常检测自动识别外观改变的特征，并将各类纹理特征自动聚类为语义连贯的组别，用于引导条件生成。整个流程从少量图像集合出发，构建出可交互使用的通用生成模型，能够对任意尺寸的纹理进行特征创建与绘制。特别地，所提出的基于扩散模型的编辑算法和无限平稳纹理生成方法具有通用性，可在其他场景中复用。项目页面：https://reality.tf.fau.de/pub/ardelean2025examplebased.html
原文摘要 · Abstract (English)
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. Project page: https://reality.tf.fau.de/pub/ardelean2025examplebased.html
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