wgsxm/PartCrafter

Health v2 · maintenance only

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2471 stars · 162 forks observed · 2026-08-28

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

PartCrafter is a structured 3D generative model that jointly generates multiple semantically meaningful 3D mesh parts and objects from a single RGB image using compositional latent diffusion transformers. It is the official PyTorch implementation of a NeurIPS 2025 paper, with pretrained checkpoints for both object-level and scene-level generation.

Use cases

• generate a 3d mesh from a single photo
• image to 3d object reconstruction
• generate 3d models with separate parts from one image
• reconstruct multi-object 3d scenes from an image
• part-aware 3d generation without image segmentation
• create editable 3d assets from pictures

When to choose

• you need structured, part-level 3D meshes from a single RGB image in one shot
• you want to avoid two-stage segment-then-reconstruct pipelines
• you need both object-level and scene-level 3D generation with pretrained checkpoints
• you want a research-grade, open-source image-to-3D model with training code

When to avoid

• you need real-time or low-latency 3D generation on CPU-only hardware
• you require production-ready 3D assets with guaranteed watertight, clean topology
• you lack a CUDA GPU or cannot handle large diffusion model checkpoints
• you need text-to-3D generation rather than image-conditioned generation

Facets

library · maturity active

machine-learning deep-learning image-processing graphics simulation artificial-intelligence computer-vision graphics deep-learning machine-learning python windows 3d-generation image-to-3d mesh-generation diffusion-transformer part-aware-3d neurips-2025 3d-reconstruction gpu linux docker

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markdown · JSON · MCP: product_card(name="wgsxm/PartCrafter")