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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