Video Generation Models Are Inherent Lighting Estimators

Recovering dynamic environment maps from a single in-the-wild video is crucial for photorealistic rendering, yet remains a challenge.Recent video generation models can produce photorealistic scenes withcomplex lighting, possessing an inherent understanding of lighting. Inthis paper, we introduce V-LITE (Video generation models are inherentlighting estimators), a framework that unlocks this internal knowledge byreframing lighting estimation as a guided video inpainting task. Inspiredby visual effects (VFX) industry practices, we insert a synthetic chromeball into the scene to compel the model to generate physically plausiblereflections from the surrounding spatio-temporal context. To bridge thegap from LDR-native models to the HDR domain, we design an HDR-aware VAE and employ an efficient LoRA-based fine-tuning strategy. Wethen construct a mixed dataset comprising high-fidelity HDR images toprovide realistic HDR priors, and in-the-wild HDR videos to provide dy-namic spatio-temporal context. Extensive experiments demonstrate thatV-LITE produces temporally coherent HDR environment maps, reveal-ing that modern video diffusion models are not merely synthesizers butalso powerful, inherently capable estimators of physical scene lighting.