CameraNoise: Enabling Faithful Camera Control in Video Diffusion through Geometry-Flow-Guided Noise Warping

2026-05-28

AI Summary

Existing video diffusion models struggle to maintain geometric consistency when precisely controlling camera poses, as directly injecting camera parameters often leads to structural distortions. This work proposes a novel approach that encodes camera motion into temporally consistent stochastic representations, implicitly embedding pose information into the noise space through a geometry-guided reprojection-based optical flow and a noise-space warping mechanism. This design decouples motion from appearance while preserving the Gaussian prior inherent in diffusion models, ensuring geometrically consistent noise propagation under camera transformations. Experimental results demonstrate that the proposed method significantly improves both visual quality and camera trajectory fidelity in generated videos, outperforming current state-of-the-art approaches.

Abstract

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/.

Problem

• camera pose control
• geometric consistency
• video diffusion
• structural distortion
• trajectory faithfulness

Innovation

• CameraNoise
• noise warping
• geometry-guided flow
• video diffusion
• camera pose control