  Accurate reconstruction of complex dynamic scenes from just a single
viewpoint continues to be a challenging task in computer vision. Current
dynamic novel view synthesis methods typically require videos from many
different camera viewpoints, necessitating careful recording setups, and
significantly restricting their utility in the wild as well as in terms of
embodied AI applications. In this paper, we propose $\textbf{GCD}$, a
controllable monocular dynamic view synthesis pipeline that leverages
large-scale diffusion priors to, given a video of any scene, generate a
synchronous video from any other chosen perspective, conditioned on a set of
relative camera pose parameters. Our model does not require depth as input, and
does not explicitly model 3D scene geometry, instead performing end-to-end
video-to-video translation in order to achieve its goal efficiently. Despite
being trained on synthetic multi-view video data only, zero-shot real-world
generalization experiments show promising results in multiple domains,
including robotics, object permanence, and driving environments. We believe our
framework can potentially unlock powerful applications in rich dynamic scene
understanding, perception for robotics, and interactive 3D video viewing
experiences for virtual reality.
