Implementation of VQ-PAE for AMASS Motion Data Processing

## Overview I've implemented a Vector-Quantized Periodic Autoencoder (VQ-PAE) for processing AMASS motion data, specifically working with HumanEva SMPLX data. The implementation extracts root-relative joint velocities and applies the Walk-the-Dog methodology for motion alignment. ## Implementation Details ### Data Processing Pipeline - **Dataset**: AMASS HumanEva SMPLX data organized in subject folders (S1, S2, S3) - **Preprocessing**: Extract root-relative joint positions and compute velocities - **Windowing**: 121-frame windows with 60-frame padding on each side - **Input Format**: (N_windows, 66, 121) - 66 joint velocity features over 121 frames ### Model Architecture - **Encoder**: 1D CNN with BatchNorm and ReLU activations - **Vector Quantizer**: Codebook with num_embeddings = number of motion types - **Amplitude MLP**: Maps latent features to quantized amplitude vectors - **Phase MLP**: Extracts phase information from flattened latent features - **Frequency MLP**: Uses FFT-based frequency estimation - **Decoder**: Transpose CNN for reconstruction ### Key Features - Automatic motion type detection from filenames - GPU acceleration with CUDA support - Periodic manifold representation with sine/cosine components - Vector quantization for discrete motion type encoding ## Current Status The implementation is functional and trains successfully. The model processes motion sequences and learns periodic representations suitable for cross-morphology motion alignment. ## Potential Areas for Enhancement 1. **Evaluation Metrics**: Add quantitative evaluation (reconstruction error, perceptual metrics) 2. **Visualization**: Implement motion visualization and latent space analysis 3. **Cross-validation**: Add proper train/validation/test splits 4. **Hyperparameter Tuning**: Optimize architecture and training parameters 5. **Motion Alignment**: Implement the cross-morphology alignment capabilities 6. **Performance Optimization**: Memory efficiency and training speed improvements ## Technical Specifications - **Input Dimensions**: 66 (22 joints × 3 coordinates) - **Window Size**: 121 frames - **Latent Dimension**: 32 - **Training**: AdamW optimizer with L2 regularization - **Loss Function**: MSE reconstruction + VQ commitment loss This implementation provides a solid foundation for exploring periodic motion representations and cross-morphology alignment as described in the Walk-the-Dog paper.