cure-lab/MagicDrive

Health v2 · maintenance only

Flags: no_releases no_readme

Adoption not part of the score

1166 stars · 52 forks observed · 2026-08-28

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

MagicDrive is the official PyTorch implementation of an ICLR 2024 paper for controllable street view generation using diffusion models with diverse 3D geometry control (BEV layouts, camera poses, text). It supports both image and video generation for autonomous driving scenarios and works with UNet and DiT architectures.

Use cases

• generate street view images from bird's-eye view layouts
• synthesize multi-camera driving videos for 3d detection training
• create augmented autonomous driving datasets with edited scenes
• generate night or rainy variants of driving scenes
• research controllable diffusion models for 3d perception

When to choose

• you need synthetic multi-camera street view data conditioned on 3D BEV geometry
• you are researching controllable diffusion generation for autonomous driving
• you want to augment nuScenes-style datasets with edited or rare scenarios

When to avoid

• you need general-purpose image generation without 3D/BEV conditioning
• you lack GPU resources for training or running large diffusion models
• you need a production-ready data pipeline rather than a research codebase

Facets

library · maturity active

image-processing video-processing machine-learning deep-learning data-generation autonomous-vehicles deep-learning computer-vision artificial-intelligence simulation python diffusion-models street-view-generation 3d-geometry-control bev data-synthesis iclr24 pytorch gpu linux

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markdown · JSON · MCP: product_card(name="cure-lab/MagicDrive")