CameraNoise: Enabling Faithful Camera Control in Video Diffusion through Geometry-Flow-Guided Noise Warping
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916064221200384 |
|---|---|
| author | Zhao, Haoyu Gu, Jiaxi Chen, Haoran Zheng, Qingping Jin, Yeying Yang, Hongyi Cheng, Junqi Zhang, Yuang Lu, Zenghui Yu, Huan Jiang, Jie Shu, Peng Wu, Zuxuan Jiang, Yu-Gang |
| author_facet | Zhao, Haoyu Gu, Jiaxi Chen, Haoran Zheng, Qingping Jin, Yeying Yang, Hongyi Cheng, Junqi Zhang, Yuang Lu, Zenghui Yu, Huan Jiang, Jie Shu, Peng Wu, Zuxuan Jiang, Yu-Gang |
| contents | Precise camera pose control is critical for video diffusion, yet maintaining geometric consistency remains a challenge. Existing methods that directly inject numerical camera parameters into the diffusion backbone often fail to bridge the gap between abstract coordinates and visual content, leading to structural distortions. To address this issue, we propose CameraNoise, a flow-to-noise warping method that encodes camera motion into a temporally coherent stochastic representation. Unlike conventional conditioning, CameraNoise embeds camera poses directly into the noise space. This decouples motion from scene appearance while faithfully preserving trajectory dynamics. Specifically, we introduce a novel Geometry-guided Reprojection Flow and a noise warping algorithm, which jointly preserve the Gaussian prior of diffusion and ensure consistent noise propagation under camera transformations. By integrating CameraNoise into the diffusion process, our framework delivers stable, high-fidelity videos. Extensive experiments demonstrate that our approach significantly outperforms prior methods in both visual quality and trajectory faithfulness. The project page and code are available at: https://gulucaptain.github.io/CameraNoise/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_30774 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CameraNoise: Enabling Faithful Camera Control in Video Diffusion through Geometry-Flow-Guided Noise Warping Zhao, Haoyu Gu, Jiaxi Chen, Haoran Zheng, Qingping Jin, Yeying Yang, Hongyi Cheng, Junqi Zhang, Yuang Lu, Zenghui Yu, Huan Jiang, Jie Shu, Peng Wu, Zuxuan Jiang, Yu-Gang Computer Vision and Pattern Recognition Precise camera pose control is critical for video diffusion, yet maintaining geometric consistency remains a challenge. Existing methods that directly inject numerical camera parameters into the diffusion backbone often fail to bridge the gap between abstract coordinates and visual content, leading to structural distortions. To address this issue, we propose CameraNoise, a flow-to-noise warping method that encodes camera motion into a temporally coherent stochastic representation. Unlike conventional conditioning, CameraNoise embeds camera poses directly into the noise space. This decouples motion from scene appearance while faithfully preserving trajectory dynamics. Specifically, we introduce a novel Geometry-guided Reprojection Flow and a noise warping algorithm, which jointly preserve the Gaussian prior of diffusion and ensure consistent noise propagation under camera transformations. By integrating CameraNoise into the diffusion process, our framework delivers stable, high-fidelity videos. Extensive experiments demonstrate that our approach significantly outperforms prior methods in both visual quality and trajectory faithfulness. The project page and code are available at: https://gulucaptain.github.io/CameraNoise/. |
| title | CameraNoise: Enabling Faithful Camera Control in Video Diffusion through Geometry-Flow-Guided Noise Warping |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.30774 |