title: CityDreamer: Compositional Generative Model of Unbounded 3D Cities
summary: In recent years, extensive research has focused on 3D natural scene
  generation, but the domain of 3D city generation has not received as much
  exploration. This is due to the greater challenges posed by 3D city generation,
  mainly because humans are more sensitive to structural distortions in urban
  environments. Additionally, generating 3D cities is more complex than 3D
  natural scenes since buildings, as objects of the same class, exhibit a wider
  range of appearances compared to the relatively consistent appearance of
  objects like trees in natural scenes. To address these challenges, we propose
  CityDreamer, a compositional generative model designed specifically for
  unbounded 3D cities, which separates the generation of building instances from
  other background objects, such as roads, green lands, and water areas, into
  distinct modules. Furthermore, we construct two datasets, OSM and GoogleEarth,
  containing a vast amount of real-world city imagery to enhance the realism of
  the generated 3D cities both in their layouts and appearances. Through
  extensive experiments, CityDreamer has proven its superiority over
  state-of-the-art methods in generating a wide range of lifelike 3D cities.
paper_authors._id: 64f53376ebd4777a561aa6df
paper_authors.name: Haozhe Xie
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-04T09:41:19.828Z
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paper_authors.fullname: Haozhe Xie
paper_authors.type: user
paper_authors.user: hzxie
paper_authors._id: 64f53376ebd4777a561aa6e0
paper_authors.name: Zhaoxi Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-04T09:41:45.485Z
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: 64f53376ebd4777a561aa6e1
paper_authors.name: Fangzhou Hong
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-04T09:42:02.547Z
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: 64f53376ebd4777a561aa6e2
paper_authors.name: Ziwei Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-04T09:42:10.949Z
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: CityDreamer: Compositional Generative Model of Unbounded 3D Cities
paper_summary: In recent years, extensive research has focused on 3D natural scene
  generation, but the domain of 3D city generation has not received as much
  exploration. This is due to the greater challenges posed by 3D city generation,
  mainly because humans are more sensitive to structural distortions in urban
  environments. Additionally, generating 3D cities is more complex than 3D
  natural scenes since buildings, as objects of the same class, exhibit a wider
  range of appearances compared to the relatively consistent appearance of
  objects like trees in natural scenes. To address these challenges, we propose
  CityDreamer, a compositional generative model designed specifically for
  unbounded 3D cities, which separates the generation of building instances from
  other background objects, such as roads, green lands, and water areas, into
  distinct modules. Furthermore, we construct two datasets, OSM and GoogleEarth,
  containing a vast amount of real-world city imagery to enhance the realism of
  the generated 3D cities both in their layouts and appearances. Through
  extensive experiments, CityDreamer has proven its superiority over
  state-of-the-art methods in generating a wide range of lifelike 3D cities.
paper_ai_summary: CityDreamer is a compositional generative model that excels in generating realistic 3D cities by separating building generation from background objects using distinct modules and leveraging real-world datasets.
paper_ai_keywords: compositional generative model 3D city generation structural distortions building instances background objects datasets OSM GoogleEarth realism