提出细粒度材料选择方法，抗光照变化，支持纹理与亚纹理两级选区。
Fine-Grained Spatially Varying Material Selection in Images
- 基于ViT多尺度特征，实现稳定精准的材料区域划分。
- 在超80万张合成图像上完成纹理与亚纹理级标注，支持双层级选择。
- 适用于图像编辑、材质替换等下游任务，对光照反射鲁棒。
本文提出一种图像中材料选择的方法，对光照和反射变化具有鲁棒性，可应用于后续图像编辑任务。该方法基于视觉变压器（ViT）模型，利用其多层次特征，并设计多分辨率处理策略，相比以往方法获得更精细、更稳定的分割结果。此外，该方法支持两个层次的选择：纹理级与亚纹理级，为此构建了新的双层级材料选择（DuMaS）数据集，包含超过80万张合成图像的密集标注，覆盖纹理与亚纹理层面。该方法为图像编辑中的细粒度材质操作提供了新范式。
原文摘要 · Abstract (English)
Selection is the first step in many image editing processes, enabling faster and simpler modifications of all pixels sharing a common modality. In this work, we present a method for material selection in images, robust to lighting and reflectance variations, which can be used for downstream editing tasks. We rely on vision transformer (ViT) models and leverage their features for selection, proposing a multi-resolution processing strategy that yields finer and more stable selection results than prior methods. Furthermore, we enable selection at two levels: texture and subtexture, leveraging a new two-level material selection (DuMaS) dataset which includes dense annotations for over 800,000 synthetic images, both on the texture and subtexture levels.
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