CityDreamer: Compositional Generative Model of Unbounded 3D Cities
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.
64f53376ebd4777a561aa6df
Haozhe Xie
admin_assigned
2023-09-04T09:41:19.828Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/63f47b5321eb234ab739e91a/vWfFNVtMkHl8gieha5PPd.jpeg
Haozhe Xie
user
hzxie
64f53376ebd4777a561aa6e0
Zhaoxi Chen
admin_assigned
2023-09-04T09:41:45.485Z
62fc8cf7ee999004b5a8b982
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Zhaoxi Chen
user
FrozenBurning
64f53376ebd4777a561aa6e1
Fangzhou Hong
admin_assigned
2023-09-04T09:42:02.547Z
623c530013a63ea865f96c8e
/avatars/164455a1a94f92b71733fc778c21bd89.svg
Fangzhou Hong
user
hongfz16
64f53376ebd4777a561aa6e2
Ziwei Liu
admin_assigned
2023-09-04T09:42:10.949Z
62ab1ac1d48b4d8b048a3473
https://cdn-avatars.huggingface.co/v1/production/uploads/1656826685333-62ab1ac1d48b4d8b048a3473.png
Ziwei Liu
user
liuziwei7
CityDreamer: Compositional Generative Model of Unbounded 3D Cities
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.
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.
compositional generative model 3D city generation structural distortions building instances background objects datasets OSM GoogleEarth realism