Papers › Learning Canonical Representations for Scene Graph to Image Generation
Learning Canonical Representations for Scene Graph to Image Generation
Roei Herzig, Amir Bar, Huijuan Xu, Gal Chechik, Trevor Darrell, Amir Globerson
Generating realistic images of complex visual scenes becomes challenging when one wishes to control the structure of the generated images. Previous approaches showed that scenes with few entities can be controlled using scene graphs, but this approach struggles as the complexity of the graph (the number of objects and edges) increases. In this work, we show that one limitation of current methods is their inability to capture semantic equivalence in graphs. We present a novel model that addresses these issues by learning canonical graph representations from the data, resulting in improved image generation for complex visual scenes. Our model demonstrates improved empirical performance on large scene graphs, robustness to noise in the input scene graph, and generalization on semantically equivalent graphs. Finally, we show improved performance of the model on three different benchmarks: Visual Genome, COCO, and CLEVR.
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Code
Syntology Ran 2 of 17 code samples harvested from 1 repository linked to this paper; 15 have no recorded run. Of those that ran: 2 ran · our draft was wrong.
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Code Syntology ran Syntology
17 samples harvested; 2 ran; 0 honoured the contract we drafted; 15 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Layout-to-Image Generation | COCO-Stuff 256x256 | AttSPADE | FID | 54.7 | #5 of 5 | Archive leaderboard | report |
| Layout-to-Image Generation | COCO-Stuff 256x256 | AttSPADE | Inception Score | 15.6 | #5 of 5 | Archive leaderboard | report |
| Layout-to-Image Generation | COCO-Stuff 256x256 | AttSPADE | LPIPS | 0.44 | #5 of 5 | Archive leaderboard | report |
| Layout-to-Image Generation | Visual Genome 256x256 | AttSPADE | FID | 36.4 | #3 of 4 | Archive leaderboard | report |
| Layout-to-Image Generation | Visual Genome 256x256 | AttSPADE | Inception Score | 11 | #3 of 4 | Archive leaderboard | report |
| Layout-to-Image Generation | Visual Genome 256x256 | AttSPADE | LPIPS | 0.51 | #3 of 4 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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