Paper Info
Title
Stylistic scene enhancement GAN: mixed stylistic enhancement generation for 3D indoor scenes.
Abstract
In this paper, we present stylistic scene enhancement GAN, SSE-GAN, a conditional Wasserstein GAN-based approach to automatic generation of mixed stylistic enhancements for 3D indoor scenes. An enhancement indicates factors that can influence the style of an indoor scene such as furniture colors and occurrence of small objects. To facilitate network training, we propose a novel enhancement feature encoding method, which represents an enhancement by a multi-one-hot vector, and effectively accommodates different enhancement factors. A Gumbel-Softmax module is introduced in the generator network to enable the generation of high fidelity enhancement features that can better confuse the discriminator. Experiments show that our approach is superior to the other baseline methods and successfully models the relationship between the style distribution and scene enhancements. Thus, although only trained with a dataset of room images in single styles, the trained generator can generate mixed stylistic enhancements by specifying multiple styles as the condition. Our approach is the first to apply a Gumbel-Softmax module in conditional Wasserstein GANs, as well as the first to explore the application of GAN-based models in the scene enhancement field.
Year
DOI
Venue
2019
10.1007/s00371-019-01691-w
The Visual Computer
Keywords
Field
DocType
Scene enhancement, 3D indoor scenes, Interior design, Conditional generative adversarial nets, Gumbel-Softmax, Multi-one-hot
High fidelity,Computer vision,Discriminator,Computer science,Interior design,Artificial intelligence,Encoding (memory)
Journal
Volume
Issue
ISSN
35
6
0178-2789
Citations 
PageRank 
References 
5
0.63
0
Authors
5
Name
Order
Citations
PageRank
Suiyun Zhang191.72
Han Zhizhong219818.28
Yu-Kun Lai3102580.48
Zwicker Matthias42513129.25
Hui Zhang518824.25