CamLit: Unified Video Diffusion with Explicit Camera and Lighting Control
On the map: Diffusion models → Video generation
Abstract (arXiv)
We present CamLit, the first unified video diffusion model that jointly performs novel view synthesis (NVS) and relighting from a single input image. Given one reference image, a user-defined camera trajectory, and an environment map, CamLit synthesizes a video of the scene from new viewpoints under the specified illumination. Within a single generative process, our model produces temporally coherent and spatially aligned outputs, including relit novel-view frames and corresponding albedo frames, enabling high-quality control of both camera pose and lighting. Qualitative and quantitative experiments demonstrate that CamLit achieves high-fidelity outputs on par with state-of-the-art methods in both novel view synthesis and relighting, without sacrificing visual quality in either task. We show that a single generative model can effectively integrate camera and lighting control, simplifying the video generation pipeline while maintaining competitive performance and consistent realism.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Video generation, one of 14 directions in Diffusion models. Builds on 43 of its 56 references that are in the map. First posted on arXiv 15 Mar 2026.
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30 more
- 2024MVSplat: Efficient 3D Gaussian Splatting from Sparse Multi-View Imagescited by 553
- 2023DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Visioncited by 514
- 2020NeRD: Neural Reflectance Decomposition from Image Collectionscited by 446
- 2024ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesiscited by 392
- 2024SV3D: Novel Multi-view Synthesis and 3D Generation from a Single Image using Latent Video Diffusioncited by 364
- 2021PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relightingcited by 332
- 2025GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera Controlcited by 325
- 2023ReconFusion: 3D Reconstruction with Diffusion Priorscited by 317
- 2024DepthSplat: Connecting Gaussian Splatting and Depthcited by 275
- 2022MM-Diffusion: Learning Multi-Modal Diffusion Models for Joint Audio and Video Generationcited by 225
- 2019Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderercited by 221
- 2025Stable Virtual Camera: Generative View Synthesis with Diffusion Modelscited by 179
- 2024LVSM: A Large View Synthesis Model with Minimal 3D Inductive Biascited by 178
- 2024RGBleftrightarrowX: Image decomposition and synthesis using material- and lighting-aware diffusion modelscited by 159
- 2021NeRF for Outdoor Scene Relightingcited by 114
- 2025VideoJAM: Joint Appearance-Motion Representations for Enhanced Motion Generation in Video Modelscited by 112
- 2024Long-LRM: Long-sequence Large Reconstruction Model for Wide-coverage Gaussian Splatscited by 111
- 2023ZeroNVS: Zero-Shot 360-Degree View Synthesis from a Single Imagecited by 109
- 2024DiLightNet: Fine-grained Lighting Control for Diffusion-based Image Generationcited by 93
- 2024Neural Gaffer: Relighting Any Object via Diffusioncited by 91
- 2024Dual Diffusion for Unified Image Generation and Understandingcited by 76
- 2024Controlling Space and Time with Diffusion Modelscited by 65
- 2024LightIt: Illumination Modeling and Control for Diffusion Modelscited by 56
- 2024Understanding the Impact of Negative Prompts: When and How Do They Take Effect?cited by 50
- 2025UniRelight: Learning Joint Decomposition and Synthesis for Video Relightingcited by 38
- 2024A Diffusion Approach to Radiance Field Relighting using Multi-Illumination Synthesiscited by 37
- 2024LumiNet: Latent Intrinsics Meets Diffusion Models for Indoor Scene Relightingcited by 37
- 2025LightLab: Controlling Light Sources in Images with Diffusion Modelscited by 36
- 2025Lux Post Facto: Learning Portrait Performance Relighting with Conditional Video Diffusion and a Hybrid Datasetcited by 28
- 2025SynthLight: Portrait Relighting with Diffusion Model by Learning to Re-render Synthetic Facescited by 24
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- 2024GenLit: Reformulating Single-Image Relighting as Video Generation
- 2026Relit-LiVE: Relight Video by Jointly Learning Environment Video
- 2023ViVid-1-to-3: Novel View Synthesis with Video Diffusion Models
- 2024CamCo: Camera-Controllable 3D-Consistent Image-to-Video Generation
- 2025DualCamCtrl: Dual-Branch Diffusion Model for Geometry-Aware Camera-Controlled Video Generation
- 2025ReLumix: Extending Image Relighting to Video via Video Diffusion Models
- 2024NVS-Solver: Video Diffusion Model as Zero-Shot Novel View Synthesizer
- 2026SyncLight: Single-Edit Multi-View Relighting