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Semantic Image Manipulation Using Scene Graphs

Dhamo H, Farshad A, Laina I, Navab N, Hager GD, Tombari F, Rupprecht C (2020)

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Publication Type: Conference contribution

Publication year: 2020

Journal

Publisher: IEEE Computer Society

Pages Range: 5212-5221

Conference Proceedings Title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Event location: Virtual, Online, USA

DOI: 10.1109/CVPR42600.2020.00526

Abstract

Image manipulation can be considered a special case of image generation where the image to be produced is a modification of an existing image. Image generation and manipulation have been, for the most part, tasks that operate on raw pixels. However, the remarkable progress in learning rich image and object representations has opened the way for tasks such as text-To-image or layout-To-image generation that are mainly driven by semantics. In our work, we address the novel problem of image manipulation from scene graphs, in which a user can edit images by merely applying changes in the nodes or edges of a semantic graph that is generated from the image. Our goal is to encode image information in a given constellation and from there on generate new constellations, such as replacing objects or even changing relationships between objects, while respecting the semantics and style from the original image. We introduce a spatio-semantic scene graph network that does not require direct supervision for constellation changes or image edits. This makes it possible to train the system from existing real-world datasets with no additional annotation effort.

Involved external institutions

How to cite

APA:

Dhamo, H., Farshad, A., Laina, I., Navab, N., Hager, G.D., Tombari, F., & Rupprecht, C. (2020). Semantic Image Manipulation Using Scene Graphs. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 5212-5221). Virtual, Online, USA: IEEE Computer Society.

MLA:

Dhamo, Helisa, et al. "Semantic Image Manipulation Using Scene Graphs." Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Virtual, Online, USA IEEE Computer Society, 2020. 5212-5221.

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