{"ID":5937242,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-09T04:54:52.777078832Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.04674","arxiv_id":"2607.04674","title":"Video Generation Models Are Inherent Lighting Estimators","abstract":"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 with complex lighting, possessing an inherent understanding of lighting. In this paper, we introduce V-LITE (Video generation models are inherent lighting estimators), a framework that unlocks this internal knowledge by reframing lighting estimation as a guided video inpainting task. Inspired by VFX industry practices, we insert a synthetic chrome ball into the scene to compel the model to generate physically plausible reflections from the surrounding spatio-temporal context. To bridge the gap from LDR-native models to the HDR domain, we design an HDR-aware VAE and employ an efficient LoRA-based fine-tuning strategy. We then construct a mixed dataset comprising high-fidelity HDR images to provide realistic HDR priors, and in-the-wild HDR videos to provide dynamic spatio-temporal context. Extensive experiments demonstrate that V-LITE produces temporally coherent HDR environment maps, revealing that modern video diffusion models are not merely synthesizers but also powerful, inherently capable estimators of physical scene lighting.","short_abstract":"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 with complex lighting, possessing an inherent understanding of lighting. In this paper, we introduce V-LITE (Video genera...","url_abs":"https://arxiv.org/abs/2607.04674","url_pdf":"https://arxiv.org/pdf/2607.04674v1","authors":"[\"Ziqi Cai\",\"Shuchen Weng\",\"Kaiqi Liu\",\"Zifeng Wang\",\"Zhiquan Zhang\",\"Minggui Teng\",\"Han Jiang\",\"Boxin Shi\"]","published":"2026-07-06T04:59:17Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Diffusion Model\",\"LoRA\",\"Variational Autoencoder\"]","has_code":false}