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A library providing foundational algorithms for human kinematic motion and numerical optimization solvers to apply human motion in various applications (GPU (CUDA) version for Linux and Windows)

Project description

Momentum

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Momentum provides foundational algorithms for human kinematic motion and numerical optimization solvers to apply human motion in various applications.

Forward and Inverse Kinematics with Interpretable Parameterization RGBD Body Tracking Solver Monocular RGB Body Tracking Solver

Quick Start

Installation

Pre-built binaries are available for Windows, macOS, and Linux:

# Python (Conda/Pixi) - Recommended
pixi add pymomentum             # Auto-detects GPU/CPU
conda install -c conda-forge pymomentum

# C++ (Conda/Pixi)
pixi add momentum-cpp
conda install -c conda-forge momentum-cpp

# Python (PyPI) - Experimental ⚠️
pip install pymomentum-cpu      # CPU version
pip install pymomentum-gpu      # GPU version with CUDA

⚠️ PyPI support is experimental. For the most stable experience, we recommend using Conda or Pixi.

📦 Browse packages: conda-forgeprefix.devPyPI

Quick Example

# Install and run
conda install -c conda-forge pymomentum
python -c "import pymomentum.geometry as geom; print(dir(geom))"

Building from Source

git clone https://github.com/facebookresearch/momentum
cd momentum
pixi run build      # Builds C++ library and Python bindings
pixi run test       # Runs tests
pixi run hello_world  # Runs example

For detailed instructions, see the comprehensive guides on our website:

📖 Documentation

Visit our documentation website for comprehensive guides, examples, and API references:

Contributing

Check our contributing guide to learn about how to contribute to the project.

License

Momentum is licensed under the MIT License. A copy of the license can be found here.

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pymomentum_gpu-0.1.107.post13-cp313-cp313-win_amd64.whl (86.0 MB view details)

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pymomentum_gpu-0.1.107.post13-cp312-cp312-win_amd64.whl (86.0 MB view details)

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pymomentum_gpu-0.1.107.post13-cp312-cp312-manylinux_2_28_x86_64.whl (34.4 MB view details)

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