Modern Architecture Style Transfer for Ruin Buildings
Journal
Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019
Pages
293-294
Date Issued
2019
Author(s)
Abstract
In this work, we focus on building style transfer, which transforms ruin buildings to modern architecture. Inspired by Gaty's and Goodfellow's style transfer and generative adversarial network (GAN), we use CycleGAN to conquer this type of problem. To avoid the artifacts and generate better images, we add “perception loss” into the network, which is the feature loss extracted by VGG pre-trained model. We also adjust cycle loss by changing the ratio of weighting parameters. Finally, we collect images of both ruin and modern architecture from websites and use unsupervised learning to train the model. The experimental results show our proposed method indeed realize the modern architecture style transfer for ruin buildings.
SDGs
Type
conference paper