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license: cc-by-4.0 |
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# GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera Control |
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CVPR 2025 (Highlight) |
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[Xuanchi Ren*](https://xuanchiren.com/), |
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[Tianchang Shen*](https://www.cs.toronto.edu/~shenti11/) |
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[Jiahui Huang](https://huangjh-pub.github.io/), |
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[Huan Ling](https://www.cs.toronto.edu/~linghuan/), |
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[Yifan Lu](https://yifanlu0227.github.io/), |
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[Merlin Nimier-David](https://merlin.nimierdavid.fr/), |
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[Thomas Müller](https://research.nvidia.com/person/thomas-muller), |
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[Alexander Keller](https://research.nvidia.com/person/alex-keller), |
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[Sanja Fidler](https://www.cs.toronto.edu/~fidler/), |
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[Jun Gao](https://www.cs.toronto.edu/~jungao/) <br> |
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\* indicates equal contribution <br> |
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**[Paper](https://arxiv.org/pdf/2503.03751), [Project Page](https://research.nvidia.com/labs/toronto-ai/GEN3C/)** |
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Abstract: We present GEN3C, a generative video model with precise Camera Control and |
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temporal 3D Consistency. Prior video models already generate realistic videos, |
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but they tend to leverage little 3D information, leading to inconsistencies, |
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such as objects popping in and out of existence. Camera control, if implemented |
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at all, is imprecise, because camera parameters are mere inputs to the neural |
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network which must then infer how the video depends on the camera. In contrast, |
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GEN3C is guided by a 3D cache: point clouds obtained by predicting the |
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pixel-wise depth of seed images or previously generated frames. When generating |
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the next frames, GEN3C is conditioned on the 2D renderings of the 3D cache with |
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the new camera trajectory provided by the user. Crucially, this means that |
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GEN3C neither has to remember what it previously generated nor does it have to |
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infer the image structure from the camera pose. The model, instead, can focus |
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all its generative power on previously unobserved regions, as well as advancing |
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the scene state to the next frame. Our results demonstrate more precise camera |
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control than prior work, as well as state-of-the-art results in sparse-view |
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novel view synthesis, even in challenging settings such as driving scenes and |
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monocular dynamic video. Results are best viewed in videos. |
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## Citation |
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``` |
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@inproceedings{ren2025gen3c, |
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title={GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera Control}, |
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author={Ren, Xuanchi and Shen, Tianchang and Huang, Jiahui and Ling, Huan and |
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Lu, Yifan and Nimier-David, Merlin and Müller, Thomas and Keller, Alexander and |
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Fidler, Sanja and Gao, Jun}, |
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booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, |
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year={2025} |
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} |
