One-2-3-45/One-2-3-45

Health v2 · maintenance only

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1718 stars · 103 forks observed · 2026-08-28

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

One-2-3-45 is the official PyTorch implementation of a NeurIPS 2023 paper that converts any single image into a full 360-degree 3D textured mesh in about 45 seconds. It uses the Zero123 view-conditioned diffusion model to generate multi-view images and an SDF-based generalizable neural surface reconstruction module to lift them into 3D, avoiding slow per-shape optimization.

Use cases

• generate a 3D mesh from a single photo
• reconstruct 3D models from images without per-shape optimization
• convert text prompts into 3D meshes via text-to-image diffusion
• create multi-view images of an object from one view
• research on generalizable neural surface reconstruction
• run image-to-3D inference via provided APIs or Hugging Face demo

When to choose

• you need fast single-image to 3D mesh generation
• you want better geometry and 3D consistency than SDS-based optimization methods
• you have an NVIDIA GPU with at least 18GB memory
• you want to extend or study feed-forward 3D reconstruction from multi-view diffusion outputs

When to avoid

• you need production-grade 3D asset pipelines with fine topology control
• you lack a CUDA GPU with sufficient VRAM
• you need real-time or mobile 3D reconstruction
• you want actively maintained software beyond the paper's lifecycle

Facets

library · maturity stable

machine-learning deep-learning image-processing graphics simulation computer-vision graphics artificial-intelligence deep-learning python 3d-generation mesh-generation nerf multi-view-reconstruction diffusion-models text-to-3d image-to-3d aigc research-code linux gpu

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