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

Generate 3D objects conditioned on text or images
https://github.com/openai/shap-e

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Generate 3D objects conditioned on text or images

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README

        

# Shap-E

This is the official code and model release for [Shap-E: Generating Conditional 3D Implicit Functions](https://arxiv.org/abs/2305.02463).

* See [Usage](#usage) for guidance on how to use this repository.
* See [Samples](#samples) for examples of what our text-conditional model can generate.

# Samples

Here are some highlighted samples from our text-conditional model. For random samples on selected prompts, see [samples.md](samples.md).




A chair that looks like an avocado


An airplane that looks like a banana


A spaceship



A chair that looks
like an avocado
An airplane that looks
like a banana
A spaceship



A birthday cupcake


A chair that looks like a tree


A green boot



A birthday cupcake
A chair that looks
like a tree
A green boot



A penguin


Ube ice cream cone


A bowl of vegetables



A penguin
Ube ice cream cone
A bowl of vegetables

# Usage

Install with `pip install -e .`.

To get started with examples, see the following notebooks:

* [sample_text_to_3d.ipynb](shap_e/examples/sample_text_to_3d.ipynb) - sample a 3D model, conditioned on a text prompt.
* [sample_image_to_3d.ipynb](shap_e/examples/sample_image_to_3d.ipynb) - sample a 3D model, conditioned on a synthetic view image. To get the best result, you should remove background from the input image.
* [encode_model.ipynb](shap_e/examples/encode_model.ipynb) - loads a 3D model or a trimesh, creates a batch of multiview renders and a point cloud, encodes them into a latent, and renders it back. For this to work, install Blender version 3.3.1 or higher, and set the environment variable `BLENDER_PATH` to the path of the Blender executable.