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MatReplace is a new reference‑free benchmark for evaluating material replacement in interior scenes, assessing edits on local material correctness, global lighting harmony, outside preservation, and inside structure. It offers three tracks that vary the conditioning signal—instruction only, instruction plus region mask, and material reference image—allowing systematic comparison of different editing approaches. Results show that while closed‑source editors excel at named‑material rendering, grounding materials from pixel references remains a significant challenge.
arXiv:2608. 06075v1 Announce Type: cross Abstract: Commercial vision-language models are reshaping computer vision, with visual priors broad enough to rival task-specific systems.
Commercial vision-language models are reshaping computer vision, with visual priors broad enough to rival task-specific systems. This raises a natural question: do they reduce the need for classic, physics-informed low-level vision?
arXiv:2607. 22705v1 Announce Type: cross Abstract: Object-centric learning aims to represent scenes as objects whose properties can be reused in new combinations.
arXiv:2606. 00188v1 Announce Type: cross Abstract: While current multimodal models are proficient at open-ended visual editing, executing precise single-answer edits remains an important obstacle.
arXiv:2608.21229v1 Announce Type: new Abstract: Omnimodal generation is central to a wide range of content creation and editing applications. In-context conditioning is essential to this paradigm. It...