title: CityDreamer4D: Compositional Generative Model of Unbounded 4D Cities
summary: 3D scene generation has garnered growing attention in recent years and has
  made significant progress. Generating 4D cities is more challenging than 3D
  scenes due to the presence of structurally complex, visually diverse objects
  like buildings and vehicles, and heightened human sensitivity to distortions in
  urban environments. To tackle these issues, we propose CityDreamer4D, a
  compositional generative model specifically tailored for generating unbounded
  4D cities. Our main insights are 1) 4D city generation should separate dynamic
  objects (e.g., vehicles) from static scenes (e.g., buildings and roads), and 2)
  all objects in the 4D scene should be composed of different types of neural
  fields for buildings, vehicles, and background stuff. Specifically, we propose
  Traffic Scenario Generator and Unbounded Layout Generator to produce dynamic
  traffic scenarios and static city layouts using a highly compact BEV
  representation. Objects in 4D cities are generated by combining stuff-oriented
  and instance-oriented neural fields for background stuff, buildings, and
  vehicles. To suit the distinct characteristics of background stuff and
  instances, the neural fields employ customized generative hash grids and
  periodic positional embeddings as scene parameterizations. Furthermore, we
  offer a comprehensive suite of datasets for city generation, including OSM,
  GoogleEarth, and CityTopia. The OSM dataset provides a variety of real-world
  city layouts, while the Google Earth and CityTopia datasets deliver
  large-scale, high-quality city imagery complete with 3D instance annotations.
  Leveraging its compositional design, CityDreamer4D supports a range of
  downstream applications, such as instance editing, city stylization, and urban
  simulation, while delivering state-of-the-art performance in generating
  realistic 4D cities.
paper_authors._id: 678897a4d42825b51c19d65a
paper_authors.name: Haozhe Xie
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-16T08:50:10.101Z
paper_authors._id: 63f47b5321eb234ab739e91a
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paper_authors.fullname: Haozhe Xie
paper_authors.type: user
paper_authors.user: hzxie
paper_authors._id: 678897a4d42825b51c19d65b
paper_authors.name: Zhaoxi Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-16T08:50:16.000Z
paper_authors._id: 62fc8cf7ee999004b5a8b982
paper_authors.avatarUrl: /avatars/6c5dda9e58747054a989f077a078f3dc.svg
paper_authors.fullname: Zhaoxi Chen
paper_authors.type: user
paper_authors.user: FrozenBurning
paper_authors._id: 678897a4d42825b51c19d65c
paper_authors.name: Fangzhou Hong
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-16T08:51:11.083Z
paper_authors._id: 623c530013a63ea865f96c8e
paper_authors.avatarUrl: /avatars/164455a1a94f92b71733fc778c21bd89.svg
paper_authors.fullname: Fangzhou Hong
paper_authors.type: user
paper_authors.user: hongfz16
paper_authors._id: 678897a4d42825b51c19d65d
paper_authors.name: Ziwei Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-16T08:50:34.647Z
paper_authors._id: 62ab1ac1d48b4d8b048a3473
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1656826685333-62ab1ac1d48b4d8b048a3473.png
paper_authors.fullname: Ziwei Liu
paper_authors.type: user
paper_authors.user: liuziwei7
paper_title: CityDreamer4D: Compositional Generative Model of Unbounded 4D Cities
paper_summary: 3D scene generation has garnered growing attention in recent years and has
  made significant progress. Generating 4D cities is more challenging than 3D
  scenes due to the presence of structurally complex, visually diverse objects
  like buildings and vehicles, and heightened human sensitivity to distortions in
  urban environments. To tackle these issues, we propose CityDreamer4D, a
  compositional generative model specifically tailored for generating unbounded
  4D cities. Our main insights are 1) 4D city generation should separate dynamic
  objects (e.g., vehicles) from static scenes (e.g., buildings and roads), and 2)
  all objects in the 4D scene should be composed of different types of neural
  fields for buildings, vehicles, and background stuff. Specifically, we propose
  Traffic Scenario Generator and Unbounded Layout Generator to produce dynamic
  traffic scenarios and static city layouts using a highly compact BEV
  representation. Objects in 4D cities are generated by combining stuff-oriented
  and instance-oriented neural fields for background stuff, buildings, and
  vehicles. To suit the distinct characteristics of background stuff and
  instances, the neural fields employ customized generative hash grids and
  periodic positional embeddings as scene parameterizations. Furthermore, we
  offer a comprehensive suite of datasets for city generation, including OSM,
  GoogleEarth, and CityTopia. The OSM dataset provides a variety of real-world
  city layouts, while the Google Earth and CityTopia datasets deliver
  large-scale, high-quality city imagery complete with 3D instance annotations.
  Leveraging its compositional design, CityDreamer4D supports a range of
  downstream applications, such as instance editing, city stylization, and urban
  simulation, while delivering state-of-the-art performance in generating
  realistic 4D cities.
paper_ai_summary: CityDreamer4D generates realistic 4D cities by separating dynamic and static elements using customized neural fields and compositional generative models.
paper_ai_keywords: compositional generative model Traffic Scenario Generator Unbounded Layout Generator BEV representation neural fields generative hash grids periodic positional embeddings scene parameterizations