mlsanityData to 14 Oct 2026

MIGS: Meta Image Generation from Scene Graphs

arXiv 2110.11918 ↗First posted 22 Oct 2021cs.CV cs.LGcited by 7

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Abstract (arXiv)

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/

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 36 of its 42 references that are in the map. First posted on arXiv 22 Oct 2021.

Builds on

36 of 42 references.

References · 36

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24 more
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Cited by year

Citing papers per year: 2022: 3; 2023: 3; 2024: 1.

7 papers.

  1. 2023SceneGenie: Scene Graph Guided Diffusion Models for Image Synthesiscited by 41
  2. 2022Latent Graph Representations for Critical View of Safety Assessmentcited by 37
  3. 2022Bending Graphs: Hierarchical Shape Matching using Gated Optimal Transportcited by 24
  4. 2024VISAGE: Video Synthesis using Action Graphs for Surgerycited by 11
  5. 2023Transformer-based Image Generation from Scene Graphscited by 8
  6. 2022DisPositioNet: Disentangled Pose and Identity in Semantic Image Manipulationcited by 4
  7. 2023PRISM: Progressive Restoration for Scene Graph-based Image Manipulationcited by 3