guochengqian/Magic123

Health v2 · maintenance only

Adoption not part of the score

1623 stars · 99 forks observed · 2026-08-28

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

Magic123 is the official PyTorch implementation of an ICLR 2024 paper that generates high-quality textured 3D meshes from a single unposed image using joint 2D and 3D diffusion priors. It uses a two-stage coarse-to-fine pipeline optimizing a NeRF then a differentiable mesh, with a tradeoff parameter controlling exploration vs exploitation of the priors.

Use cases

• generate a 3D model from a single photo
• reconstruct a textured 3D mesh from one image
• create 3D assets from concept art
• convert 2D images into 3D objects for games
• research on image-to-3D generation with diffusion priors
• control creativity vs fidelity in single-image 3D reconstruction

When to choose

• you need to turn a single unposed image into a textured 3D mesh
• you want a tunable tradeoff between imaginative (2D prior) and precise (3D prior) geometry
• you are reproducing or building on ICLR 2024 image-to-3D research
• you have a CUDA GPU and Ubuntu environment

When to avoid

• you need fast real-time 3D generation - optimization takes significant GPU time
• you lack a CUDA-capable GPU
• you need production-grade 3D asset tooling rather than research code
• you only have multi-view images, where classical photogrammetry is more appropriate

Facets

library · maturity stable

machine-learning deep-learning image-processing graphics simulation computer-vision graphics artificial-intelligence deep-learning python image-to-3d 3d-generation diffusion-models nerf pytorch research-code iclr-2024 linux gpu

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