TY - JOUR
T1 - Semantic Image Manipulation Using Scene Graphs
AU - Dhamo, Helisa
AU - Farshad, Azade
AU - Laina, Iro
AU - Navab, Nassir
AU - Hager, Gregory D.
AU - Tombari, Federico
AU - Rupprecht, Christian
N1 - Funding Information:
Acknowledgements We gratefully acknowledge the Deutsche Forschungsgemeinschaft (DFG) for supporting this research work, under the project #381855581. Christian Rupprecht is supported by ERC IDIU-638009.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
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U2 - 10.1109/CVPR42600.2020.00526
DO - 10.1109/CVPR42600.2020.00526
M3 - Conference article
AN - SCOPUS:85094837436
SP - 5212
EP - 5221
JO - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
JF - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SN - 1063-6919
M1 - 9157808
T2 - 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020
Y2 - 14 June 2020 through 19 June 2020
ER -