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README.md

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-license: cc-by-4.0
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+---
+license: cc-by-4.0
+---
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+# GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera Control
+CVPR 2025 (Highlight)
+
+[Xuanchi Ren*](https://xuanchiren.com/),
+[Tianchang Shen*](https://www.cs.toronto.edu/~shenti11/)
+[Jiahui Huang](https://huangjh-pub.github.io/),
+[Huan Ling](https://www.cs.toronto.edu/~linghuan/),
+[Yifan Lu](https://yifanlu0227.github.io/),
+[Merlin Nimier-David](https://merlin.nimierdavid.fr/),
+[Thomas Müller](https://research.nvidia.com/person/thomas-muller),
+[Alexander Keller](https://research.nvidia.com/person/alex-keller),
+[Sanja Fidler](https://www.cs.toronto.edu/~fidler/),
+[Jun Gao](https://www.cs.toronto.edu/~jungao/) <br>
+\* indicates equal contribution <br>
+**[Paper](https://arxiv.org/pdf/2503.03751), [Project Page](https://research.nvidia.com/labs/toronto-ai/GEN3C/)**
+
+Abstract: 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.
+
+## Citation
+```
+ @inproceedings{ren2025gen3c,
+    title={GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera Control},
+    author={Ren, Xuanchi and Shen, Tianchang and Huang, Jiahui and Ling, Huan and 
+        Lu, Yifan and Nimier-David, Merlin and Müller, Thomas and Keller, Alexander and 
+        Fidler, Sanja and Gao, Jun},
+    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+    year={2025}
+}