Sarthak Garg * Helisa Dhamo * Azade Farshad Sabrina Musatian Nassir Navab Federico Tombari
Technical University of Munich Google
* The first two authors contributed equally.
Scene graphs, composed of nodes as objects and directed-edges as relationships among objects, offer an alternative representation of a scene that is more semantically grounded than images. We hypothesize that a generative model for scene graphs might be able to learn the underlying semantic structure of real-world scenes more effectively than images, and hence, generate realistic novel scenes in the form of scene graphs. In this work, we explore a new task for the unconditional generation of semantic scene graphs using a generative approach. For this task, a deep auto-regressive model called SceneGraphGen is developed, which can directly learn the probability distribution over labelled and directed graphs using a hierarchical recurrent architecture. We demonstrate the application of the generated graphs in image synthesis, anomaly detection and scene graph completion.
@inproceedings{scenegraphgen2021,
title={Unconditional Scene Graph Generation},
author={Garg, Sarthak and Dhamo, Helisa and Farshad, Azade and Musatian, Sabrina and Navab, Nassir and Tombari, Federico},
booktitle={IEEE International Conference on Computer Vision (ICCV)},
year={2021}
}
The cleaned scene graph dataset used in our paper, based on Visual Genome, can be downloaded here.
The source code for this work can be found here.
For questions regarding the method or the source code, please contact sarthak.garg[at]tum.de.