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MIGS: Meta Image Generation from Scene Graphs

22 Oct 2021arXiv:2110.11918archive 2025-07-28

Azade Farshad, Sabrina Musatian, Helisa Dhamo, Nassir Navab

Generation of images from scene graphs is a promising direction towards explicit scene generation and manipulation. However, the images generated from the scene graphs lack quality, which in part comes due to high difficulty and diversity in the data. We propose MIGS (Meta Image Generation from Scene Graphs), a meta-learning based approach for few-shot image generation from graphs that enables adapting the model to different scenes and increases the image quality by training on diverse sets of tasks. By sampling the data in a task-driven fashion, we train the generator using meta-learning on different sets of tasks that are categorized based on the scene attributes. Our results show that using this meta-learning approach for the generation of images from scene graphs achieves state-of-the-art performance in terms of image quality and capturing the semantic relationships in the scene. Project Website: https://migs2021.github.io/

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migs2021/migs officialmentioned on GitHubpytorch report

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Tasks

DiversityImage GenerationImage Generation from Scene GraphsMeta-LearningScene Generation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Generation from Scene Graphs BDD100K-Subsets MIGS FID 49.5 #1 of 2 Archive leaderboard report
Image Generation from Scene Graphs BDD100K-Subsets SG2Im FID 66.1 #2 of 2 Archive leaderboard report
Image Generation from Scene Graphs Home Action Genome MIGS FID 98.1 #1 of 2 Archive leaderboard report
Image Generation from Scene Graphs Home Action Genome SG2Im FID 141.3 #2 of 2 Archive leaderboard report
Image Generation from Scene Graphs Visual Genome 64x64 MIGS FID 54.24 #1 of 2 Archive leaderboard report
Image Generation from Scene Graphs Visual Genome 64x64 SG2Im FID 55.2 #2 of 2 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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