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Shap-e

Craft 3D experiences from text/images with collaborative tools.
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What is Shap-e?

Shap-E is a system that generates 3D objects conditioned on text or images. Shap-E uses a diffusion process to generate a 3D image from a text prompt, and it was introduced in the paper “Shap-E: Generating Conditional 3D Implicit Functions” by Heewoo Jun and Alex Nichol from OpenAI. The repository includes guidance on how to use the code, examples of what the text-conditional model can generate, and instructions for installation and usage. Users can install the package with `pip install -e .` and find examples in notebooks like `sample_text_to_3d.ipynb` and `sample_image_to_3d.ipynb`. The system is designed to generate complex and diverse 3D assets in a matter of seconds, and it is capable of converging faster and reaching comparable or better sample quality than Point-E, an explicit generative model over point clouds.

 


 

⚡Top 5 Shap-E Features:

1. Text-conditional model: Shap-E can generate 3D objects conditioned on text prompts, allowing users to create 3D models based on text descriptions.
2. Image-conditional model: Shap-E can also generate 3D objects conditioned on synthetic view images, which can be used to create 3D models based on 2D images.
3. Implicit function generation: Shap-E generates the parameters of implicit functions that can be rendered as both textured meshes and neural radiance fields, providing flexibility in rendering options.
4. Encoder and diffusion model: Shap-E uses an encoder to map 3D assets into the parameters of an implicit function, followed by a conditional diffusion model to generate the final 3D model.
5. Fast convergence and sample quality: Shap-E converges faster than Point-E, an explicit generative model over point clouds, and achieves comparable or better sample quality despite modeling a higher-dimensional, multi-representation output space.

 


 

⚡Top 5 Shap-E Use Cases:

1. Text-to-3D model: Shap-E can be used to generate 3D models based on text prompts, which can be useful in various applications such as architectural design, product visualization, and game development.
2. Image-to-3D model: Shap-E can convert 2D images into 3D models, which can be used for tasks like object recognition, 3D reconstruction, and virtual reality content creation.
3. Conditional rendering: Shap-E’s ability to generate 3D models conditioned on text or images allows for the creation of custom 3D assets tailored to specific needs or preferences.
4. Multi-representation output space: Shap-E’s support for both textured meshes and neural radiance fields enables users to choose the most suitable rendering method for their application.
5. Fast training and inference: Shap-E’s fast convergence and sample quality make it a practical choice for generating 3D models in a variety of domains, including art, engineering, and entertainment.

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