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In recent years, extensive research has focused on 3D natural scenegeneration, but the domain of 3D city generation has not received as muchexploration. This is due to the greater challenges posed by 3D city generation,mainly because humans are more sensitive to structural distortions in urbanenvironments. Additionally, generating 3D cities is more complex than 3Dnatural scenes since buildings, as objects of the same class, exhibit a widerrange of appearances compared to the relatively consistent appearance ofobjects like trees in natural scenes. To address these challenges, we proposeCityDreamer, a compositional generative model designed specifically forunbounded 3D cities, which separates the generation of building instances fromother background objects, such as roads, green lands, and water areas, intodistinct modules. Furthermore, we construct two datasets, OSM and GoogleEarth,containing a vast amount of real-world city imagery to enhance the realism ofthe generated 3D cities both in their layouts and appearances. Throughextensive experiments, CityDreamer has proven its superiority overstate-of-the-art methods in generating a wide range of lifelike 3D cities.

CityDreamer: Compositional Generative Model of Unbounded 3D Cities