CamCo: Camera-Controllable 3D-Consistent Image-to-Video Generation
Authors: name: Dejia Xu, name: Weili Nie, name: Chao Liu, name: Sifei Liu, name: Jan Kautz, name: Zhangyang Wang, name: Arash Vahdat

Recently video diffusion models have emerged as expressive generative tools
for high-quality video content creation readily available to general users.
However, these models often do not offer precise control over camera poses for
video generation, limiting the expression of cinematic language and user
control. To address this issue, we introduce CamCo, which allows fine-grained
Camera pose Control for image-to-video generation. We equip a pre-trained
image-to-video generator with accurately parameterized camera pose input using
Pl\"ucker coordinates. To enhance 3D consistency in the videos produced, we
integrate an epipolar attention module in each attention block that enforces
epipolar constraints to the feature maps. Additionally, we fine-tune CamCo on
real-world videos with camera poses estimated through structure-from-motion
algorithms to better synthesize object motion. Our experiments show that CamCo
significantly improves 3D consistency and camera control capabilities compared
to previous models while effectively generating plausible object motion.
Project page: https://ir1d.github.io/CamCo/