Shap-E - AI Vision Models Tool

Overview

Shap-E is an open-source generative model and reference implementation from OpenAI that produces 3D implicit-function representations conditioned on text or images. Instead of outputting only point clouds or voxels, Shap-E directly generates the parameters of implicit functions that can be rendered as textured polygonal meshes or neural radiance fields (NeRFs). The project includes pretrained model weights, inference code, and example notebooks to run text-to-3D and image-to-3D demos, enabling rapid sampling of novel 3D assets in seconds on suitably provisioned hardware. ([arxiv.org](https://arxiv.org/abs/2305.02463)) Shap-E’s pipeline is a two-stage approach: an encoder maps existing 3D assets into latent parameters of an implicit function, and a conditional diffusion model is trained on those encodings so it can generate new implicit-function parameters from a text or image conditioning signal. The repository provides practical notebooks (sample_text_to_3d.ipynb, sample_image_to_3d.ipynb, encode_model.ipynb) and instructions for converting model outputs to common formats (PLY, textured meshes) and rendering GIF previews; some workflows use Blender for multiview rendering. While powerful, users report that installation and dependency configuration (PyTorch/PyTorch3D, CUDA, Blender) can be nontrivial and that outputs may require post-processing for high-resolution 3D printing or game-ready assets. ([arxiv.org](https://arxiv.org/abs/2305.02463))

GitHub Statistics

• Stars: 12,183
• Forks: 1,058
• Contributors: 7
• License: MIT
• Primary Language: Python
• Last Updated: 2023-11-08T17:19:41Z

The official GitHub repository (openai/shap-e) is actively used as the canonical release for code and model weights. The repo has roughly 12.2k stars and ~1.1k forks, and lists seven contributors; issues and community threads show ongoing user questions and troubleshooting requests. There are a modest number of commits and pull requests since the initial release (the repository contains sample notebooks and model-card documentation). This pattern — high interest (many stars/forks) but limited core-maintainer activity — is typical for research-code releases that publish model weights and examples but don’t follow a heavy long-term product release cadence. ([github.com](https://github.com/openai/shap-e))

Installation

Install via pip:

git clone https://github.com/openai/shap-e.git

cd shap-e

pip install -e .

Optional (recommended on Linux/WSL): create a Conda env and install PyTorch with CUDA, e.g. conda create -n shap-e python=3.9 && conda activate shap-e && conda install pytorch=1.13.0 torchvision pytorch-cuda=11.6 -c pytorch -c nvidia. (See Tom's Hardware notes for a tested example.)

Key Features

• Generates parameters of implicit functions renderable as textured meshes or NeRFs.
• Text-conditioned generation: sample 3D assets directly from prompts.
• Image-conditioned generation: encode multi-view renders or single views to condition outputs.
• Two-stage pipeline: deterministic encoder + conditional diffusion generative model.
• Example notebooks for sampling, encoding, and exporting PLY/mesh outputs (Blender recommended).

Community

Shap-E has substantial community interest (≈12.2k stars, ≈1.1k forks, 7 contributors) and an open issue tracker where users report installation, runtime, and output-quality questions. Community ports and integrations (e.g., Stable Diffusion UI extensions, Hugging Face/Replicate demos) exist, and press reviews highlight promising results but note heavy GPU requirements and setup fragility. Active community discussion and issue threads are useful for troubleshooting. ([github.com](https://github.com/openai/shap-e))

Key Information

• Category: Vision Models
• Type: AI Vision Models Tool