nielsr HF Staff commited on
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Enhance dataset card: Add descriptive tags and detailed sample usage

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This PR enhances the dataset card by:
- Adding more descriptive `tags` (`3d-physics`, `material-properties`, `gaussian-splatting`, `clip-features`, `3d-assets`) to the metadata for better discoverability. These tags are derived from the paper's abstract and the project's GitHub README, highlighting key aspects of the dataset and its applications.
- Expanding the "Sample Usage" section to include a Python code snippet demonstrating how to use the dataset and models with the `pipeline.py` script from the associated GitHub repository. This provides a more complete and actionable example of using the artifact beyond just data download.

Files changed (1) hide show
  1. README.md +21 -3
README.md CHANGED
@@ -1,7 +1,13 @@
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  ---
 
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  task_categories:
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  - image-to-3d
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- license: mit
 
 
 
 
 
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  ---
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  # Pixie Dataset
@@ -20,13 +26,25 @@ This dataset contains data and pre-trained models for the paper [Pixie: Fast and
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  ## Sample Usage
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- Use the download script in the Pixie repository to automatically download this data:
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  ```bash
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  python scripts/download_data.py
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  ```
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- For a full pipeline usage, refer to the [Github repository's usage section](https://github.com/vlongle/pixie#usage).
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Citation
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  ---
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+ license: mit
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  task_categories:
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  - image-to-3d
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+ tags:
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+ - 3d-physics
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+ - material-properties
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+ - gaussian-splatting
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+ - clip-features
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+ - 3d-assets
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  ---
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  # Pixie Dataset
 
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  ## Sample Usage
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+ First, use the download script in the Pixie repository to automatically download this data and models:
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  ```bash
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  python scripts/download_data.py
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  ```
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+ Then, you can run the main pipeline with a synthetic Objaverse object, for example:
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+
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+ ```python
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+ python pipeline.py obj_id=f420ea9edb914e1b9b7adebbacecc7d8 material_mode=neural
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+ ```
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+ This command will:
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+ 1. Download the specified Objaverse asset.
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+ 2. Render it and train 3D representations (NeRF, Gaussian Splatting).
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+ 3. Generate a voxel feature grid.
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+ 4. Use the trained neural networks to predict the physics field.
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+ 5. Run the MPM physics solver using the predicted physics parameters.
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+
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+ For more detailed usage, including real-scene processing and training, refer to the [Github repository's usage section](https://github.com/vlongle/pixie#usage).
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  ## Citation
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