mlsanityData to 14 Oct 2026

Fine-Grained Spatially Varying Material Selection in Images

arXiv 2506.09023 ↗First posted 10 Jun 2025cs.GR cs.CVcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 23 of its 47 references that are in the map. First posted on arXiv 10 Jun 2025.

Builds on

23 of 47 references.

References · 23

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11 more
  1. 2023Contrastive Lift: 3D Object Instance Segmentation by Slow-Fast Contrastive Fusioncited by 64
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Phieat: Physically Grounded Material Feature Representationcited by 0
  2. 2026Material Magic Wand: Material-Aware Grouping of 3D Parts in Untextured Meshescited by 0
  3. 2026MAOAM: Unified Object and Material Selection with Vision-Language Modelscited by 0