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

FrankenGAN - Guided Detail Synthesis for Building Mass Models Using Style-Synchonized GANs from SmartGeometry on YouTube.

Description

FrankenGAN: Guided Detail Synthesis for Building Mass-Models using Style-Synchonized GANs Tom Kelly (UCL and University of Leeds), Paul Guerrero (UCL), Anthony Steed (UCL), Peter Wonka (KAUST), Niloy J. Mitra (UCL) SIGGRAPH Asia 2018

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.

References

Kelly, T., Guerrero, P., Steed, A., Wonka, P., & Mitra, N. J. (2018). FrankenGAN: guided detail synthesis for building mass-models using style-synchonized GANs. arXiv preprint arXiv:1806.07179.

Website

FrankenGAN: Guided Detail Synthesis for Building Mass Models using Style-Synchonized GANs: http://geometry.cs.ucl.ac.uk/projects/2018/frankengan/