# FrankenGAN: Guided Detail Synthesis for Building Mass-Models Using Style-Synchonized GANs

arXiv: https://arxiv.org/abs/1806.07179 · HTML: https://mlsanity.com/paper/1806.07179/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2018.

## Built on by (most cited first, 5 of 5)

- [Patch-based 3D Natural Scene Generation from a Single Example](https://mlsanity.com/paper/2304.12670/) (2023)
- [Leveraging Generative AI for Urban Digital Twins: A Scoping Review on the Autonomous Generation of Urban Data, Scenarios, Designs, and 3D City Models for Smart City Advancement](https://mlsanity.com/paper/2405.19464/) (2024)
- [Semi-Supervised Adversarial Recognition of Refined Window Structures for Inverse Procedural Faccade Modeling](https://mlsanity.com/paper/2201.08977/) (2022)
- [Projective Urban Texturing](https://mlsanity.com/paper/2201.10938/) (2022)
- [Seamless Satellite-image Synthesis](https://mlsanity.com/paper/2111.03384/) (2021)

## Abstract (arXiv)

Coarse building mass models are now routinely generated at scales ranging from individual buildings through to whole cities. For example, they can be abstracted from raw measurements, generated procedurally, or created manually. However, these models typically lack any meaningful semantic or texture details, making them unsuitable for direct display. We introduce the problem of automatically and realistically decorating such models by adding semantically consistent geometric details and textures. Building on the recent success of generative adversarial networks (GANs), we propose FrankenGAN, a cascade of GANs to create plausible details across multiple scales over large neighborhoods. The various GANs are synchronized to produce consistent style distributions over buildings and neighborhoods. We provide the user with direct control over the variability of the output. We allow her to interactively specify style via images and manipulate style-adapted sliders to control style variability. We demonstrate our system on several large-scale examples. The generated outputs are qualitatively evaluated via a set of user studies and are found to be realistic, semantically-plausible, and style-consistent.