title: Streetscapes: Large-scale Consistent Street View Generation Using
    Autoregressive Video Diffusion
summary: 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.
paper_authors._id: 6699e2fdeeefee89b1bbd18b
paper_authors.name: Boyang Deng
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-08-31T21:22:39.498Z
paper_authors._id: 66d3896c6839bd191d0c5ad3
paper_authors.avatarUrl: /avatars/34acf042c946b5e7144ce3c002c9939f.svg
paper_authors.fullname: Boyang Deng
paper_authors.type: user
paper_authors.user: bydeng
paper_authors._id: 6699e2fdeeefee89b1bbd18c
paper_authors.name: Richard Tucker
paper_authors._id: 6699e2fdeeefee89b1bbd18d
paper_authors.name: Zhengqi Li
paper_authors._id: 6699e2fdeeefee89b1bbd18e
paper_authors.name: Leonidas Guibas
paper_authors._id: 6699e2fdeeefee89b1bbd18f
paper_authors.name: Noah Snavely
paper_authors._id: 6699e2fdeeefee89b1bbd190
paper_authors.name: Gordon Wetzstein
paper_title: Streetscapes: Large-scale Consistent Street View Generation Using
    Autoregressive Video Diffusion
paper_summary: 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.
paper_ai_summary: A method synthesizes long-range Streetscapes video sequences from city layouts and language inputs using conditioned video diffusion within an autoregressive framework.
paper_ai_keywords: video diffusion autoregressive framework temporal imputation city layouts language input Google Street View