Title: CameraNoise: Learning Precise Camera Control with Video Diffusion in Noise Space
Primary area: generative models
Keywords: Diffusion Model, Video Generation, CameraNoise

Abstract: Controlling camera pose in video diffusion models is essential for generating realistic videos, yet existing approaches struggle to achieve precise control. Methods that directly inject numerical camera parameters into the diffusion backbone often fail to capture subtle viewpoint variations and lead to structural distortions or visual artifacts. To overcome these limitations, we propose CameraNoise, a temporally coherent stochastic representation warped from camera intrinsic and extrinsic parameters. Unlike conventional approaches, CameraNoise embeds camera poses directly into the noise space. This makes our approach independent of scene appearance while faithfully encoding camera motion. Specifically, we introduce a novel Geometry-guided Reprojection Flow along with a CameraNoise warping algorithm, which jointly preserves the Gaussian prior of diffusion and ensures consistent noise propagation under camera transformations. By integrating CameraNoise into the diffusion process, our framework delivers stable and high-quality videos with precise camera control. Extensive experiments on the RealEstate10K benchmark demonstrate that our approach significantly outperforms prior methods in both fidelity and controllability.

TL;DR: We propose CameraNoise, a stochastic noise-space representation derived from camera parameters that preserves the Gaussian prior and enables temporally coherent, appearance-independent, and camera-controllable video generation with diffusion model.