Learning Latent Proxies for Controllable Single-Image Relighting

TL;DR

This paper introduces LightCtrl, a method for controllable single-image relighting that uses sparse physical cues and a new dataset to achieve photometrically faithful results with precise control, surpassing prior approaches.

Contribution

We propose a novel approach that leverages sparse physical cues and a lighting-aware mask within a diffusion model for improved controllable relighting, along with a new large-scale dataset.

Findings

Achieves up to +2.4 dB PSNR improvement over baselines.

Reduces RMSE by 35% under controlled lighting shifts.

Outperforms prior diffusion and intrinsic-based methods.

Abstract

Single-image relighting is highly under-constrained: small illumination changes can produce large, nonlinear variations in shading, shadows, and specularities, while geometry and materials remain unobserved. Existing diffusion-based approaches either rely on intrinsic or G-buffer pipelines that require dense and fragile supervision, or operate purely in latent space without physical grounding, making fine-grained control of direction, intensity, and color unreliable. We observe that a full intrinsic decomposition is unnecessary and redundant for accurate relighting. Instead, sparse but physically meaningful cues, indicating where illumination should change and how materials should respond, are sufficient to guide a diffusion model. Based on this insight, we introduce LightCtrl that integrates physical priors at two levels: a few-shot latent proxy encoder that extracts compact material-geometry cues from limited PBR supervision, and a lighting-aware mask that identifies sensitive illumination regions and steers the denoiser toward shading relevant pixels. To compensate for scarce PBR data, we refine the proxy branch using a DPO-based objective that enforces physical consistency in the predicted cues. We also present ScaLight, a large-scale object-level dataset with systematically varied illumination and complete camera-light metadata, enabling physically consistent and controllable training. Across object and scene level benchmarks, our method achieves photometrically faithful relighting with accurate continuous control, surpassing prior diffusion and intrinsic-based baselines, including gains of up to +2.4 dB PSNR and 35% lower RMSE under controlled lighting shifts.