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,...
The paper introduces a counterfactual benchmark to evaluate whether vision models understand physical constraints or merely learn rendering shortcuts. Using Mitsuba 3, it generates 5,500 synthetic images across 500 scene families, each with a clean image and ten violations (shadows, reflections, support, surface response, occlusion). Diagnostic baselines (LLaVA‑1.5‑7B, Qwen2.5‑VL‑7B, InternVL3.5‑8B) achieve 64.0–67.8 % macro‑F1 on a held‑out synthetic test set, but performance drops to 40.8 % for LLaVA‑1.5‑7B under an intervention shift, indicating reliance on rendering cues rather than true physical reasoning.
By M. Moein Esfahani, Sepehr Salem, Mohammed Alser, Vince Calhoun
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.
By Shilin Hu, Jingyi Xu, Dimitris Samaras, Hieu Le
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.
By Anuraag Gadehothur Karnam, Tarunesh Sathish
SREdit is a response‑guided framework that improves projection‑based appearance editing by estimating a scene response map from a reconstructed scene. This map indicates how strongly projected illumination can affect different surface regions, allowing the system to adapt editing strength across the object while preserving registration with the fixed physical surface. Experiments with two pretrained image editors show that SREdit enhances projected appearance quality and reproduction fidelity, adding less than one second per image with negligible VRAM overhead, and participants consistently preferred its results in a user study.
By Yuqiao Xia, Yuchen Deng, Ziheng Chang, Bingyao Huang