# Light-A-Video: Training-free Video Relighting via Progressive Light Fusion

arXiv: https://arxiv.org/abs/2502.08590 · HTML: https://mlsanity.com/paper/2502.08590/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models.
- First posted on arXiv 12 Feb 2025.

## Placed in

- Diffusion models / [3D, point cloud and scene generation](https://mlsanity.com/diffusion/3d-point-cloud-and-scene-generation/)

## Built on by (most cited first, 30 of 39)

- [WoW: Towards a World omniscient World model Through Embodied Interaction](https://mlsanity.com/paper/2509.22642/) (2025)
- [CapRL: Stimulating Dense Image Caption Capabilities via Reinforcement Learning](https://mlsanity.com/paper/2509.22647/) (2025)
- [Omni-Effects: Unified and Spatially-Controllable Visual Effects Generation](https://mlsanity.com/paper/2508.07981/) (2025)
- [HiFlow: Training-free High-Resolution Image Generation with Flow-Aligned Guidance](https://mlsanity.com/paper/2504.06232/) (2025)
- [Lumen: Consistent Video Relighting and Harmonious Background Replacement with Video Generative Models](https://mlsanity.com/paper/2508.12945/) (2025)
- [UniLumos: Fast and Unified Image and Video Relighting with Physics-Plausible Feedback](https://mlsanity.com/paper/2511.01678/) (2025)
- [Light-X: Generative 4D Video Rendering with Camera and Illumination Control](https://mlsanity.com/paper/2512.05115/) (2025)
- [TC-Light: Temporally Coherent Generative Rendering for Realistic World Transfer](https://mlsanity.com/paper/2506.18904/) (2025)
- [LuxRemix: Lighting Decomposition and Remixing for Indoor Scenes](https://mlsanity.com/paper/2601.15283/) (2026)
- [RelightMaster: Precise Video Relighting with Multi-plane Light Images](https://mlsanity.com/paper/2511.06271/) (2025)
- [IllumiCraft: Unified Geometry and Illumination Diffusion for Controllable Video Generation](https://mlsanity.com/paper/2506.03150/) (2025)
- [LiveLight: Real-time Streaming Video Relighting with Interactive Control](https://mlsanity.com/paper/2608.01771/) (2026)
- [FlowPortal: Residual-Corrected Flow for Training-Free Video Relighting and Background Replacement](https://mlsanity.com/paper/2511.18346/) (2025)
- [Learning User Preferences for Image Generation Model](https://mlsanity.com/paper/2508.08220/) (2025)
- [ANYPORTAL: Zero-Shot Consistent Video Background Replacement](https://mlsanity.com/paper/2509.07472/) (2025)
- [GR3EN: Generative Relighting for 3D Environments](https://mlsanity.com/paper/2601.16272/) (2026)
- [LightCtrl: Training-free Controllable Video Relighting](https://mlsanity.com/paper/2603.27083/) (2026)
- [From Rigging to Waving: 3D-Guided Diffusion for Natural Animation of Hand-Drawn Characters](https://mlsanity.com/paper/2509.06573/) (2025)
- [Light4D: Training-Free Extreme Viewpoint 4D Video Relighting](https://mlsanity.com/paper/2602.11769/) (2026)
- [Lighting-grounded Video Generation with Renderer-based Agent Reasoning](https://mlsanity.com/paper/2604.07966/) (2026)
- [ReLumix: Extending Image Relighting to Video via Video Diffusion Models](https://mlsanity.com/paper/2509.23769/) (2025)
- [Learning Latent Proxies for Controllable Single-Image Relighting](https://mlsanity.com/paper/2603.15555/) (2026)
- [Learning Sidewalk Autopilot from Multi-Scale Imitation with Corrective Behavior Expansion](https://mlsanity.com/paper/2603.22527/) (2026)
- [Control-DINO: Feature Space Conditioning for Controllable Image-to-Video Diffusion](https://mlsanity.com/paper/2604.01761/) (2026)
- [Relit-LiVE: Relight Video by Jointly Learning Environment Video](https://mlsanity.com/paper/2605.06658/) (2026)
- [ID-V2V: Identity-Preserving Video Restylization](https://mlsanity.com/paper/2607.22830/) (2026)
- [HPSD: Hybrid-Policy Self-Distillation for Text-Image-to-Video Diffusion Models](https://mlsanity.com/paper/2608.13205/) (2026)
- [Shedding Light: A Benchmark for Evaluating Lighting Understanding in Generative Image Models](https://mlsanity.com/paper/2609.10787/) (2026)
- [Hi-Light: A Path to high-fidelity, high-resolution video relighting with a Novel Evaluation Paradigm](https://mlsanity.com/paper/2601.23167/) (2026)
- [GO-Renderer: Generative Object Rendering with 3D-aware Controllable Video Diffusion Models](https://mlsanity.com/paper/2603.23246/) (2026)

## Abstract (arXiv)

Recent advancements in image relighting models, driven by large-scale datasets and pre-trained diffusion models, have enabled the imposition of consistent lighting. However, video relighting still lags, primarily due to the excessive training costs and the scarcity of diverse, high-quality video relighting datasets. A simple application of image relighting models on a frame-by-frame basis leads to several issues: lighting source inconsistency and relighted appearance inconsistency, resulting in flickers in the generated videos. In this work, we propose Light-A-Video, a training-free approach to achieve temporally smooth video relighting. Adapted from image relighting models, Light-A-Video introduces two key techniques to enhance lighting consistency. First, we design a Consistent Light Attention (CLA) module, which enhances cross-frame interactions within the self-attention layers of the image relight model to stabilize the generation of the background lighting source. Second, leveraging the physical principle of light transport independence, we apply linear blending between the source video's appearance and the relighted appearance, using a Progressive Light Fusion (PLF) strategy to ensure smooth temporal transitions in illumination. Experiments show that Light-A-Video improves the temporal consistency of relighted video while maintaining the relighted image quality, ensuring coherent lighting transitions across frames. Project page: https://bujiazi.github.io/light-a-video.github.io/.