mlsanityData to 14 Oct 2026

MatReplace: A Reference-Free, Conditioning-Aligned Benchmark for Material Replacement in Interior Scenes

arXiv 2608.24107 ↗First posted 25 Aug 2026cs.CV cs.AIcited by 0

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Abstract (arXiv)

Material replacement is a common interior-design operation: changing the material of a selected surface while preserving its geometry, surroundings, and illumination. Despite its commercial relevance, no public benchmark isolates this task, and evaluating it is challenging. Reference-based metrics penalize valid outputs in this inherently one-to-many setting, favor the style of the reference generator, and cannot fairly compare editors that receive different forms of guidance. We introduce MatReplace, a reference-free benchmark that evaluates edits along four verifiable dimensions: local material correctness, global lighting harmony, outside preservation, and inside structure. It defines three tracks that vary one conditioning signal at a time: (A) instruction only, (B) instruction plus region mask, and (C) material reference image instead of instruction. Our results reveal a clear divide between naming and visually grounding materials. In Track A, leading closed-source editors achieve exemplar-level material rendering and surpass the exemplar anchor under our primary aggregate. In Track B, masks help only mask-compatible models with weak scene preservation, with task-paired, single-seed effects ranging from +0.137 to -0.090 across aligned model families. In Track C, reference-image conditioning degrades every family under both aggregates, by -0.031 to -0.508; in the worst cases, models repaint the reference image itself and perform worse than returning the input unchanged. Thus, named-material rendering is largely solved by the strongest closed editors on this distribution, but grounding materials from pixels remains an open challenge. Expert ratings validate our ranking (Kendall's tau = 0.68) and align with our aggregates more closely than GT-referenced or CLIP-based baselines.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 29 references that are in the map. First posted on arXiv 25 Aug 2026.

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References · 23

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11 more
  1. 2020Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understandingcited by 604
  2. 2022Paint by Example: Exemplar-based Image Editing with Diffusion Modelscited by 489
  3. 2023AnyDoor: Zero-shot Object-level Image Customizationcited by 412
  4. 20203D-FRONT: 3D Furnished Rooms with layOuts and semaNTicscited by 380
  5. 2023Emu Edit: Precise Image Editing via Recognition and Generation Taskscited by 324
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  10. 2024ZeST: Zero-Shot Material Transfer from a Single Imagecited by 24
  11. 2025MatSwap: Light-aware material transfers in imagescited by 6

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