Shedding Light: A Benchmark for Evaluating Lighting Understanding in Generative Image Models — Paper

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 https://lvsn.github.io/SheddingLight/ .

Authors 5

Justine Giroux, Jack Oliver Hilliard, Yannick Hold-Geoffroy, Javier Vazquez-Corral, Jean-Fran\c{c}ois Lalonde

Specification

Official page: arxiv.org/abs/2609.10787
Source:arXiv (Atom API + RSS)T1observed 50 min agohigh
Arxiv announce type: new
Source:arXiv (Atom API + RSS)T1observed 50 min agohigh
arXiv id: 2609.10787
Source:arXiv (Atom API + RSS)T1observed 50 min agohigh
Categories: cs.CV
Source:arXiv (Atom API + RSS)T1observed 50 min agohigh
DOI: 10.1145/3842579
Source:arXiv (Atom API + RSS)T1observed 50 min agohigh
PDF: arxiv.org/pdf/2609.10787
Source:arXiv (Atom API + RSS)T1observed 50 min agohigh
Primary category: cs.CV
Source:arXiv (Atom API + RSS)T1observed 50 min agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 50 min agohigh

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Provenance

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10

Source tiers

T110

Freshest observation

50 min ago

Conflicts

None

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Authors: Justine Giroux, Jack Oliver Hilliard, Yannick Hold-Geoffroy

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10 claims · 10 properties

Official pageofficial_url1

Claim history for Official page
Value	Valid from → to	Status	Source	Confidence	Extractor
https://arxiv.org/abs/2609.10787	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Abstractabstract1

Claim history for Abstract
Value	Valid from → to	Status	Source	Confidence	Extractor
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 https://lvsn.github.io/SheddingLight/ .	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Arxiv announce typearxiv_announce_type1

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new	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

arXiv idarxiv_id1

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2609.10787	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Authorsauthors1

Claim history for Authors
Value	Valid from → to	Status	Source	Confidence	Extractor
Justine Giroux, Jack Oliver Hilliard, Yannick Hold-Geoffroy, Javier Vazquez-Corral, Jean-Fran\c{c}ois Lalonde	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Categoriescategories1

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cs.CV	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

DOIdoi1

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Value	Valid from → to	Status	Source	Confidence	Extractor
10.1145/3842579	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

PDFpdf_url1

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https://arxiv.org/pdf/2609.10787	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Primary categoryprimary_category1

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cs.CV	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Publishedpublished_at1

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11 Sept 2026	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
New paperPaperShedding Light: A Benchmark for Evaluating Lighting Understanding in Generative Image Models
New paper: Shedding Light: A Benchmark for Evaluating Lighting Understanding in Generative Image Models
arxiv
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Source documents
Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.CV	feed	T1· Official	50 min ago	1

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