google/sg2im

Health v2 · maintenance only

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Adoption not part of the score

1325 stars · 229 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A PyTorch research implementation of the CVPR 2018 paper 'Image Generation from Scene Graphs' by Johnson et al. It converts a structured scene graph of objects and relationships into a synthetic image using graph convolution networks, layout prediction, and cascaded refinement networks trained adversarially.

Use cases

• generate images from scene graphs
• control object layout in generated images
• reproduce the sg2im paper results
• experiment with graph convolution networks for image synthesis
• generate images conditioned on object relationships
• research structured image generation models

When to choose

• you need to generate images from structured scene graph descriptions
• you want fine-grained control over objects and their relationships in generated images
• you are reproducing or building on the CVPR 2018 sg2im paper
• you need a PyTorch baseline for layout-based image synthesis

When to avoid

• you need modern high-fidelity text-to-image generation like diffusion models
• you want a production-supported Google product
• you need compatibility with recent PyTorch versions without modification
• you need pretrained models for domains beyond the paper's datasets

Facets

library · maturity maintenance

machine-learning deep-learning image-processing graphics machine-learning computer-vision image-processing deep-learning python scene-graph image-generation gan pytorch research-code cvpr-2018 graph-convolution-network linux gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="google/sg2im")