GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera Control
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Cited by 102 in Diffusion models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
We present GEN3C, a generative video model with precise Camera Control and temporal 3D Consistency. Prior video models already generate realistic videos, but they tend to leverage little 3D information, leading to inconsistencies, such as objects popping in and out of existence. Camera control, if implemented at all, is imprecise, because camera parameters are mere inputs to the neural network which must then infer how the video depends on the camera. In contrast, GEN3C is guided by a 3D cache: point clouds obtained by predicting the pixel-wise depth of seed images or previously generated frames. When generating the next frames, GEN3C is conditioned on the 2D renderings of the 3D cache with the new camera trajectory provided by the user. Crucially, this means that GEN3C neither has to remember what it previously generated nor does it have to infer the image structure from the camera pose. The model, instead, can focus all its generative power on previously unobserved regions, as well as advancing the scene state to the next frame. Our results demonstrate more precise camera control than prior work, as well as state-of-the-art results in sparse-view novel view synthesis, even in challenging settings such as driving scenes and monocular dynamic video. Results are best viewed in videos. Check out our webpage! https://research.nvidia.com/labs/toronto-ai/GEN3C/
Key facts. Cited by 325 papers in the map (data through 7 Oct 2026). Builds on 66 of its 79 references that are in the map. First posted on arXiv 5 Mar 2025.
Builds on
66 of 79 references.
References · 66
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- 2023FreeNeRF: Improving Few-shot Neural Rendering with Free Frequency Regularizationcited by 310
- 2021Dense Depth Priors for Neural Radiance Fields from Sparse Input Viewscited by 301
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- 2023SparseNeRF: Distilling Depth Ranking for Few-shot Novel View Synthesiscited by 247
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- 2024Gaussian Opacity Fields: Efficient Adaptive Surface Reconstruction in Unbounded Scenescited by 226
- 2023EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-Supervisioncited by 216
- 2024DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videoscited by 202
- 20244D-Rotor Gaussian Splatting: Towards Efficient Novel View Synthesis for Dynamic Scenescited by 185
- 2024LVSM: A Large View Synthesis Model with Minimal 3D Inductive Biascited by 178
50 most cited of 66 All 66 (JSON)
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Citing papers per year: 2025: 120; 2026: 205.
325 papers.
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