BBDC 2022: A guide for executing the code of the team Kornstante
Firstly, the requirements given in requirements.txt have to be installed.
The data given in the challenge has to be located in a data directory within the root directory.
General approach
Mocap: Was solved using a one layer LSTM, with a history of 105 and prediction length of one frame.
Video: Was treated as an inbetweening problem. Two convolutional LSTMs were trained. One predicts in the forward direction, the other one predicts in the backward direction.
ConvLSTM forward: Takes 15 frames in front of a given gap as it's history and predicts the following 5 frames.
ConvLSTM backward: Firstly, the data is reversed along the time axis. The network takes 15 frames after a gap as it's history and predicts the 5 frames in front of the history frames.
The mocap and video prediction are carried out using a sliding window over the missing frames.
Training the models
Run the preprocessing script preprocessing.py
The sequence lengths for the model input and label are passed as command-line arguments
The arguments are parsed and passed to the function calls
The mocap model is trained in the mocap_train.py file
The training is configured via global variables in the file, e.g. BS for the batch size
The trained model is saved to the best_model_mocap folder
Training the video prediction model
The video prediction is a bi-directional temporal reconstruction
The "forward" model is trained in video_train.py
The "reverse" model is trained via reverse_video_train.py
Both trained models are saved to their respective folders
torch_models/convlstm/ and torch_models/reverse_convlstm/
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