AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。
[Submitted on 9 Sep 2026] Title:Shedding Light: A Benchmark for Evaluating Lighting Understanding in Generative Image Models View a PDF of the paper titled Shedding Light: A Benchmark for Evaluating Lighting Understanding in Generative Image Models, by Justine Giroux and 4 other authors View PDF HTML (experimental) Abstract:Accurate modelling of illumination is central to realistic image synthesis and scene understanding. Yet, there is little exploration into whether image generative models are good at this task or whether physical plausibility remains a key challenge for them. Clearly, significant progress has been made in realistic image synthesis, but do models truly understand lighting in a physically accurate manner? To answer this question, this work proposes a benchmark to assess the lighting understanding and harmonisation capabilities of generative models. Our key insight is that evaluating lighting understanding for such models only requires testing how well they insert novel objects into real photographs whilst maintaining consistent illumination. To do so, we use a multi-illumination dataset with images containing simple objects serving as ``light probes'', and prompt models to inpaint the same object onto the original image, then compare the generated results against the ground-truth light probes. We then estimate the lighting direction, colour and radiance distribution from the inpainted probes, providing a quantitative measure of illumination accuracy and photometric realism. Our work establishes a scalable evaluation protocol to systematically assess how well generative models capture and reproduce real-world lighting, offering a foundation for benchmarking the photometric accuracy of any future models. All code and data are available at this https URL . Comments: Accepted to ACM Transactions on Graphics (SIGGRAPH Asia 2026), vol. 45, no. 6, article 227, December 2026. 25 pages. Project page: this https URL Subjects: Computer Vision and Pattern Recognition (cs.CV) ACM classes: I.2.10; I.4.8; I.3.7; I.3.3 Cite as: arXiv:2609.10787 [cs.CV] (or arXiv:2609.10787v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2609.10787 arXiv-issued DOI via DataCite (pending registration) Related DOI: https://doi.org/10.1145/3842579 DOI(s) linking to related resources Submission history From: Justine Giroux [view email] [v1] Wed, 9 Sep 2026 19:49:30 UTC (42,675 KB) Full-text links: Access Paper: View a PDF of the paper titled Shedding Light: A Benchmark for Evaluating Lighting Understanding in Generative Image Models, by Justine Giroux and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV new | recent | 2026-09 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)