  We present a method for text-driven perpetual view generation -- synthesizing
long-term videos of various scenes solely, given an input text prompt
describing the scene and camera poses. We introduce a novel framework that
generates such videos in an online fashion by combining the generative power of
a pre-trained text-to-image model with the geometric priors learned by a
pre-trained monocular depth prediction model. To tackle the pivotal challenge
of achieving 3D consistency, i.e., synthesizing videos that depict
geometrically-plausible scenes, we deploy an online test-time training to
encourage the predicted depth map of the current frame to be geometrically
consistent with the synthesized scene. The depth maps are used to construct a
unified mesh representation of the scene, which is progressively constructed
along the video generation process. In contrast to previous works, which are
applicable only to limited domains, our method generates diverse scenes, such
as walkthroughs in spaceships, caves, or ice castles.
