  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.
