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.
Figures & tables
Figure 2: An illustration of CamLit pipeline. At the core of our framework is a multi-modal video DiT. This model takes as input a single RGB image, a camera trajectory, and an environment map. From these inputs, the model simultaneously generates a spatially and temporally aligned triplet of videos: (i) an RGB novel-view sequence under the same illumination as the input image, (ii) the corresponding relit sequence (with full shading from the environment map), and (iii) an albedo sequence capturing the scene’s intrinsics without shading.
Figure 3: Video generation examples of CamLit. For each example, we visualize two camera trajectories, moving backward and turning right as indicated by the arrows, to reveal generated content in unseen regions. From left to right, we show the input image, a novel view frame under the original lighting, the corresponding albedo, and three relit novel view frames. The environment maps used for relighting are shown in the insets.
NVS Methods
FID ↓
SEVA [ 56 ]
5.615
GEN3C [ 35 ]
5.144
Our nvs-only
6.724
Ours
7.213
Relighting Methods
FID ↓
DiffusionRenderer [ 22 ]
14.088
Table 1: FID scores computed on RealEstate10K [ 57 ] test split. Our unified architecture performs NVS and relighting jointly, yet achieves fidelity on par with state-of-the-art approaches dedicated to each task.
NVS Methods
FVD ↓
SEVA [ 56 ]
94.95
GEN3C [ 35 ]
91.69
Our nvs-only
78.03
Ours
99.17
Relighting Methods
FVD ↓
DiffusionRenderer [ 22 ]
103.98
Table 2: FVD scores computed on RealEstate10K [ 57 ] test split. Our unified system achieves video quality comparable to state-of-the-art approaches dedicated to each task.
Figure 4: Qualitative comparison of novel view synthesis methods. For each input image, we apply two camera trajectories, moving backward ( 1 st row) and turning right ( 2 nd row), as indicated by the arrows. Our model, which performs NVS and relighting jointly, achieves NVS quality on par with state-of-the-art methods specifically dedicated to NVS.
Figure 5: Qualitative comparison of relighting methods. Our approach produces albedo and relit videos with quality comparable to DiffusionRenderer [ 22 ] , which is our theoretical performance upper bound. The environment maps used for relighting are shown in the middle insets.
Figure 6: Qualitative comparison of our NVS‑only and full models. Two camera trajectories, moving backward and turning left as indicated by arrow directions. Both models produce high‑quality, geometrically consistent novel views, demonstrating that the unified formulation preserves NVS performance while integrating relighting.