SceneScape: Text-Driven Consistent Scene Generation

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We present a method for text-driven perpetual view generation – synthesizing
long-term videos of various scenes solely from 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.


Rafail Fridman (Weizmann Institute of Science)
Amit Abecasis (Weizmann Institute of Science)
Tali Dekel (Weizmann Institute of Science)

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