To incorporate temporal information into the proposed skeletal transformer for further enhancing the robustness and accuracy of the motion capture pipeline, a few key strategies can be implemented:
Temporal Consistency: By introducing a temporal component to the skeletal transformer, the model can leverage information from previous frames to ensure consistency in pose and shape estimation over time. This can help in smoothing out predictions and reducing jitter in the output.
Recurrent Neural Networks (RNNs): Utilizing RNNs or Long Short-Term Memory (LSTM) networks can enable the model to capture temporal dependencies in the input data. This can help in predicting future poses based on the sequence of past poses, leading to more accurate and coherent motion capture results.
Temporal Attention Mechanisms: Implementing attention mechanisms that focus on relevant frames in a sequence can improve the model's ability to extract meaningful temporal information. By attending to key frames with significant pose changes or occlusions, the model can make more informed predictions.
Temporal Data Augmentation: Introducing temporal data augmentation techniques, such as frame interpolation or time warping, can help the model generalize better to variations in motion speed and timing. This can enhance the model's ability to handle different motion dynamics.
Temporal Loss Functions: Designing loss functions that penalize temporal inconsistencies between consecutive frames can guide the model to produce smoother and more coherent motion sequences. By enforcing temporal continuity in the predicted poses, the model can generate more realistic and accurate motion capture results.