Papers › CityDreamer: Compositional Generative Model of Unbounded 3D Cities

CityDreamer: Compositional Generative Model of Unbounded 3D Cities

1 Sep 2023CVPR 2024 1arXiv:2309.00610archive 2025-07-28

Haozhe Xie, Zhaoxi Chen, Fangzhou Hong, Ziwei Liu

3D city generation is a desirable yet challenging task, since 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 \textbf{CityDreamer}, a compositional generative model designed specifically for unbounded 3D cities. Our key insight is that 3D city generation should be a composition of different types of neural fields: 1) various building instances, and 2) background stuff, such as roads and green lands. Specifically, we adopt the bird's eye view scene representation and employ a volumetric render for both instance-oriented and stuff-oriented neural fields. The generative hash grid and periodic positional embedding are tailored as scene parameterization to suit the distinct characteristics of building instances and background stuff. Furthermore, we contribute a suite of CityGen Datasets, including OSM and GoogleEarth, which comprises a vast amount of real-world city imagery to enhance the realism of the generated 3D cities both in their layouts and appearances. CityDreamer achieves state-of-the-art performance not only in generating realistic 3D cities but also in localized editing within the generated cities.

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1ran · our draft was wrong
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collate_fn hzxie/CityDreamer/utils/datasets.py official repository ran licence not identified · pointer only · 806801bc58bb3023 · report
nonlinearity hzxie/CityDreamer/models/vqgan.py official repository ran · our draft was wrong fingerprinted no licence file found · pointer only · 3137073275f8c21a · report
affine_registration hzxie/CityDreamer/metrics/camera_error.py official repository unverified licence not identified · pointer only · 93cb61d92976e7fe · report
get_colmap_cam_pose hzxie/CityDreamer/metrics/camera_error.py official repository unverified licence not identified · pointer only · d200de1ccb86fa3f · report
get_connectivity_index hzxie/CityDreamer/metrics/urban_planning.py official repository unverified licence not identified · pointer only · 90f9b7eddb796ec2 · report
init_dist hzxie/CityDreamer/utils/distributed.py official repository unverified no licence file found · pointer only · b5d91bbfdb1eb2da · report
normalize hzxie/CityDreamer/models/vqgan.py official repository unverified licence not identified · pointer only · 2d4d03c6f604240d · report
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vgg19 hzxie/CityDreamer/losses/perceptual.py official repository unverified no licence file found · pointer only · 396918e041c75412 · report

Tasks

Scene Generationmodel

Datasets

Introduced by this paper, per the archive.

GoogleEarthOSM

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Scene Generation GoogleEarth CityDreamer Camera Error 0.060 #2 of 5 Archive leaderboard report
Scene Generation GoogleEarth CityDreamer Depth Error 0.147 #2 of 5 Archive leaderboard report
Scene Generation GoogleEarth CityDreamer FID 97.38 #2 of 5 Archive leaderboard report
Scene Generation GoogleEarth CityDreamer KID 0.096 #2 of 5 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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