Learning Latent Proxies for Controllable Single-Image Relighting

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-based 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 full intrinsic decomposition is unnecessary 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 minimal 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 illumination-sensitive 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 aligns predicted cues with perceptually preferred relighting behavior. We further 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.