ML Anthology

LayoutVAE: Stochastic Scene Layout Generation from a Label Set

Akash Abdu Jyothi, Thibaut Durand, Jiawei He, Leonid Sigal, Greg Mori
ICCV 2019
doi:10.1109/ICCV.2019.00999 /iccv/2019/jyothi2019iccv-layoutvae/

Abstract

Recently there is an increasing interest in scene generation within the research community. However, models used for generating scene layouts from textual description largely ignore plausible visual variations within the structure dictated by the text. We propose LayoutVAE, a variational autoencoder based framework for generating stochastic scene layouts. LayoutVAE is a versatile modeling framework that allows for generating full image layouts given a label set, or per label layouts for an existing image given a new label. In addition, it is also capable of detecting unusual layouts, potentially providing a way to evaluate layout generation problem. Extensive experiments on MNIST-Layouts and challenging COCO 2017 Panoptic dataset verifies the effectiveness of our proposed framework.

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Text

Jyothi et al. "LayoutVAE: Stochastic Scene Layout Generation from a Label Set." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019. doi:10.1109/ICCV.2019.00999

Markdown

[Jyothi et al. "LayoutVAE: Stochastic Scene Layout Generation from a Label Set." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019.](https://mlanthology.org/iccv/2019/jyothi2019iccv-layoutvae/) doi:10.1109/ICCV.2019.00999

BibTeX

@inproceedings{jyothi2019iccv-layoutvae,
  title     = {{LayoutVAE: Stochastic Scene Layout Generation from a Label Set}},
  author    = {Jyothi, Akash Abdu and Durand, Thibaut and He, Jiawei and Sigal, Leonid and Mori, Greg},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
  year      = {2019},
  doi       = {10.1109/ICCV.2019.00999},
  url       = {https://mlanthology.org/iccv/2019/jyothi2019iccv-layoutvae/}
}