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Generate 3D objects conditioned on text or images

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Shap-E Samples Usage

README.md

 Shap-E

This is the official code and model release for Shap-E: Generating
Conditional 3D Implicit Functions.

  * See Usage for guidance on how to use this repository.
  * See 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.

 A chair that looks like  An airplane that looks like   A spaceship
       an avocado                  a banana
   A chair that looks       An airplane that looks      A spaceship
     like an avocado             like a banana
   A birthday cupcake      A chair that looks like a   A green boot
                                     tree
   A birthday cupcake         A chair that looks       A green boot
                                  like a tree
        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 - sample a 3D model, conditioned on a
    text prompt
  * sample_image_to_3d.ipynb - sample a 3D model, conditioned on an
    synthetic view image.
  * 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.

About

Generate 3D objects conditioned on text or images

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Readme

License

MIT license

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Contributors 4

  * @unixpickle unixpickle Alex Nichol
  * @heewooj heewooj Heewoo Jun
  * @bitsnaps bitsnaps Ibrahim H.
  * @ahmadmustafaanis ahmadmustafaanis Ahmad Mustafa Anis

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  * Python 98.3%
  * Jupyter Notebook 1.7%

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