Streetscapes: Large-scale Consistent Street View Generation Using
  Autoregressive Video Diffusion
Authors: name: Boyang Deng, name: Richard Tucker, name: Zhengqi Li, name: Leonidas Guibas, name: Noah Snavely, name: Gordon Wetzstein

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.