Decoupled Illumination Priors for Spatially Controllable Multi-View Indoor Scene Relighting

2026-07-09 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation, Computer Vision & Graphics · Depth: Expert, quick

Summary

Lume-Palette is a progressive framework designed for spatially controllable multi-view indoor scene relighting, addressing the need for photorealism, precise spatial control, and multi-view consistency. Traditional diffusion-based image editing models often struggle with exact 3D light placement, disrupting their generative priors. Lume-Palette decouples relighting into two stages: first, illumination distillation extracts canonical illumination palettes from a pretrained diffusion model to preserve realistic material-light interactions. Second, illumination casting explicitly maps target spatial lighting conditions derived from coarse 3D geometry. The framework also employs an asymmetric multi-view conditioning strategy to efficiently manage dense multi-view and multi-modal inputs. Experiments on diverse synthetic and real-world scenes confirm that Lume-Palette achieves photorealistic, spatially controllable, and multi-view consistent relighting results.

Key takeaway

For computer vision engineers or 3D graphics developers implementing realistic indoor scene relighting, Lume-Palette offers a robust framework to achieve photorealism, precise spatial control, and multi-view consistency. Its decoupled approach, separating illumination distillation from explicit spatial casting, overcomes limitations of traditional diffusion models in exact 3D light placement. You should consider this framework for projects demanding high-fidelity, controllable lighting across multiple views in indoor environments.

Key insights

Lume-Palette decouples illumination priors from spatial casting to achieve photorealistic, spatially controllable, and multi-view consistent indoor scene relighting.

Principles

• Decouple complex relighting into distinct stages.
• Leverage pretrained diffusion models for realism.
• Explicitly map spatial lighting conditions.

Method

Lume-Palette's method involves illumination distillation from a pretrained diffusion model, followed by illumination casting using coarse 3D geometry. An asymmetric multi-view conditioning strategy handles dense inputs.

In practice

• Generate photorealistic indoor scene relighting.
• Ensure multi-view consistency in lighting.
• Precisely control 3D light placement.

Topics

• Indoor Scene Relighting
• Diffusion Models
• Multi-View Synthesis
• 3D Geometry
• Illumination Priors
• Spatial Control

Best for: Research Scientist, AI Scientist, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.