English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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.

Stylistic scene enhancement GAN: mixed stylistic enhancement generation for 3D indoor scenes