Streetscapes: Large-scale Consistent Street View Generation Using
  Autoregressive Video Diffusion
We present a method for generating Streetscapes-long sequences of views
through an on-the-fly synthesized city-scale scene. Our generation is
conditioned by language input (e.g., city name, weather), as well as an
underlying map/layout hosting the desired trajectory. Compared to recent models
for video generation or 3D view synthesis, our method can scale to much
longer-range camera trajectories, spanning several city blocks, while
maintaining visual quality and consistency. To achieve this goal, we build on
recent work on video diffusion, used within an autoregressive framework that
can easily scale to long sequences. In particular, we introduce a new temporal
imputation method that prevents our autoregressive approach from drifting from
the distribution of realistic city imagery. We train our Streetscapes system on
a compelling source of data-posed imagery from Google Street View, along with
contextual map data-which allows users to generate city views conditioned on
any desired city layout, with controllable camera poses. Please see more
results at our project page at https://boyangdeng.com/streetscapes.
6699e2fdeeefee89b1bbd18b
Boyang Deng
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2024-08-31T21:22:39.498Z
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Boyang Deng
user
bydeng
6699e2fdeeefee89b1bbd18c
Richard Tucker
6699e2fdeeefee89b1bbd18d
Zhengqi Li
6699e2fdeeefee89b1bbd18e
Leonidas Guibas
6699e2fdeeefee89b1bbd18f
Noah Snavely
6699e2fdeeefee89b1bbd190
Gordon Wetzstein
Streetscapes: Large-scale Consistent Street View Generation Using
  Autoregressive Video Diffusion
We present a method for generating Streetscapes-long sequences of views
through an on-the-fly synthesized city-scale scene. Our generation is
conditioned by language input (e.g., city name, weather), as well as an
underlying map/layout hosting the desired trajectory. Compared to recent models
for video generation or 3D view synthesis, our method can scale to much
longer-range camera trajectories, spanning several city blocks, while
maintaining visual quality and consistency. To achieve this goal, we build on
recent work on video diffusion, used within an autoregressive framework that
can easily scale to long sequences. In particular, we introduce a new temporal
imputation method that prevents our autoregressive approach from drifting from
the distribution of realistic city imagery. We train our Streetscapes system on
a compelling source of data-posed imagery from Google Street View, along with
contextual map data-which allows users to generate city views conditioned on
any desired city layout, with controllable camera poses. Please see more
results at our project page at https://boyangdeng.com/streetscapes.
A method synthesizes long-range Streetscapes video sequences from city layouts and language inputs using conditioned video diffusion within an autoregressive framework.
video diffusion autoregressive framework temporal imputation city layouts language input Google Street View