Human Motion Generation from Keypoints using Deep Learning
⚠️ This project was a great learning experience, but I have since moved on to other priorities. The code remains available here for educational purposes, but expect no further updates.
MotionForge-AI is a deep learning project focused on generating smooth and realistic human motion sequences from raw pose keypoints.
The system processes keypoint data, handles missing values, and learns temporal patterns to reconstruct motion dynamics.
- Convert raw pose keypoints into structured motion sequences
- Improve motion smoothness and temporal consistency
- Build a scalable pipeline for motion-based AI applications
- ✅ Keypoint preprocessing & normalization
- ✅ Missing joint interpolation
- ✅ Sliding window sequence generation
- ✅ Deep learning model for motion prediction
- 🚧 Motion refinement (in progress)
Raw Keypoints
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Preprocessing & Cleaning
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Interpolation (Missing Joints)
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Normalization
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Sequence Windowing
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Deep Learning Model
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Generated Motion
- Python
- PyTorch
- NumPy
- OpenCV
- MediaPipe
The project is still under active development.
Current limitations:
- Motion output is not fully smooth
- Sensitive to noisy keypoints
- Temporal consistency needs improvement
- Implement LSTM / Transformer-based architectures
- Improve feature engineering & normalization
- Add motion smoothing techniques
- Integrate with 3D animation tools (Blender / game engines)
- Animation & character movement
- Motion analysis systems
- Human behavior modeling
- AI-based simulation environments
Contributions, ideas, and feedback are welcome. Feel free to open issues or submit pull requests.
If you’d like to collaborate or discuss ideas, feel free to connect.
