Frontiers in Communication· 2026Q2
AI-based relighting in moving images: current potentials, limitations, and implications for cinematographic practice
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- Q2SCImago
- 2026year
Short summary
AI-based relighting tools (Nvidia DiffusionRenderer, Beeble Studio, SwitchX) show potential for selective correction and extension in film post-production, particularly for wide shots, but do not currently replace on-set lighting due to limitations in workflow integration and detail retention in close-ups.
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Key points
- AI relighting tools are best suited for selective correction and extension, not as a full replacement for on-set lighting.
- Strengths are observed in wide and establishing shots where fine details are less critical.
- Limitations include workflow integration issues, texture loss, and image alterations, particularly in portrait shots.
- Expert interviews highlighted concerns about visual credibility and the need for continuity of cinematographic expertise in post-production.
AI-generated from the title and abstract; the full text is not read.
Abstract
The rapid development of Artificial Intelligence (AI) continuously opens up new possibilities in film post-production, including the subsequent relighting of moving images. As a systematic assessment from a film-design and professional-practice perspective has been lacking, this study examines the potentials, limitations, and implications of current AI-based relighting workflows for cinematographic practice. Methodologically, the study follows a qualitative-exploratory research design within practice-based and practice-led frameworks. Three current AI-based relighting systems (Nvidia DiffusionRenderer, Beeble Studio, and SwitchX) were tested across five test shots and evaluated using a cinematography-oriented qualitative evaluation scheme focusing on light plausibility, image quality and texture retention, as well as image integrity and temporal stability. These observations were complemented by eight semi-structured expert interviews with professionals from cinematography, lighting, and color grading. Results suggest that AI-based relighting does not currently replace on-set lighting but offers considerable potential as a selective correction and extension tool. Its strengths are particularly evident in wide and establishing shots, where faces and fine image details are less perceptually salient, as well as in situations where the desired lighting conditions are difficult to achieve or maintain on set due to weather, time, or budget constraints. Technical and creative limitations are most apparent in workflow integration and usability, as well as in texture loss and image alterations that impair visual credibility, particularly in portrait shots. The findings suggest the potential for a selective shift of certain lighting decisions toward post-production rather than a general replacement of on-set lighting. To maintain a coherent lighting design when substantial lighting decisions are deferred to post-production, greater continuity of cinematographic expertise and creative agency across production and post-production may be required.
The authors' abstract, as published at the source. Frontiers in Communication, 2026 · DOI ↗
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Field: Computer Graphics and Computer-Aided Design
Computer Graphics and Computer-Aided DesignComputer Science