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Modeling and designing urban building layouts is of significant interest in computer vision, computer graphics, and urban applications. A building layout consists of a set of buildings in city blocks defined by a network of roads. We observe that building layouts are discrete structures, consisting of multiple rows of buildings of various shapes, and are amenable to skeletonization for mapping arbitrary city block shapes to a canonical form. Hence, we propose a fully automatic approach to building layout generation using graph attention networks. Our method generates realistic urban layouts given arbitrary road networks, and enables conditional generation based on learned priors. Our results, including user study, demonstrate superior performance as compared to prior layout generation networks, support arbitrary city block and varying building shapes as demonstrated by generating layouts for 28 large cities.
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Urban Brush: Intuitive and Controllable Urban Layout Editing
Efficient urban layout generation is an interesting and important problem in many applications dealing with computer graphics and entertainment. We introduce a novel framework for intuitive and controllable small and large-scale urban layout editing. The key inspiration comes from the observation that cities develop in small incremental changes e.g., a building is replaced, or a new road is created. We introduce a set of atomic operations that consistently modify the city. For example, two buildings are merged, a block is split in two, etc. Our second inspiration comes from volumetric editings, such as clay manipulation, where the manipulated material is preserved. The atomic operations are used in interactive brushes that consistently modify the urban layout. The city is populated with agents. Like volume transfer, the brushes attract or repulse the agents, and blocks can be merged and populated with smaller buildings. We also introduce a large-scale brush that repairs a part of the city by learning style as distributions of orientations and intersections.
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- Award ID(s):
- 1816514
- PAR ID:
- 10378396
- Date Published:
- Journal Name:
- UIST '21: The 34th Annual ACM Symposium on User Interface Software and Technology
- Page Range / eLocation ID:
- 796 to 814
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Modeling and designing urban building layouts is of significant interest in computer vision, computer graphics, and urban applications. A building layout consists of a set of buildings in city blocks defined by a network of roads. We observe that building layouts are discrete structures, consisting of multiple rows of buildings of various shapes, and are amenable to skeletonization for mapping arbitrary city block shapes to a canonical form. Hence, we propose a fully automatic approach to building layout generation using graph attention networks. Our method generates realistic urban layouts given arbitrary road networks, and enables conditional generation based on learned priors. Our results, including user study, demonstrate superior performance as compared to prior layout generation networks, support arbitrary city block and varying building shapes as demonstrated by generating layouts for 28 large cities.more » « less
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3472749.3474787
