Materials

compilations

Dataset

H3.6m

Human3.6M

Parse

Paper

Depth camera

Doc

Human motion prediction using recurrent neural networks

Repository
Paper

Input

raw data : for each subjects(S1,S2 …) , each action(walking, waiting, smoking …), each sub sequence(1/2):
$(n) \times 99$ (np.ndarray, float32)

From data_utils.load_data() used by translate.read_all_data()

train data: the composed dictionary ((suject_id, action, subaction_id, ‘even’) as key) of raw data (just even rows), with one hot encoding columns for action type, if action is specified (normal case), just append an all 1 column to rawdata. Size of each dictionary value:
$(n/2) \times (99 + actions;count)$

complete data: all data joint together, from different subjects, actions, sub sequences:
$(n) \times 99$

From translate.read_all_data() used by translate.train()

train set : normalized train data, throw out data with $std < 1e-4$ (accroding to complete data). Size of each dictionary value:
$(n/2) \times ((99-used;dimension;count) + actions;count)$

Human Dimension

After the analyzztion of the complete data, human dimension has been fixed to $54$.

From Seq2SeqModel.get_batch() used by translate.train()

total_seq: $60$ ($[0,59]$)

source_seq_len: $50$
target_seq_len: $10$

batch_size: $16$

encoder_inputs: $16\times 49\times (54+actions;count)$
Interpretation: [batch,frame,dimension]
frame range: $[0,48]$

decoder_inputs: $16\times 10\times (54+actions;count)$
frame range: $[49,58]$

decoder_outputs: $16\times 10\times (54+actions;count)$
frame range: $[50,59]$

Model prediction input

encoder_inputs: Tensor form of encoder_inputs from Seq2SeqModel.get_batch()

1

torch.from_numpy(encoder_inputs).float()

decoder_inputs: Tensor form of decoder_inputs from Seq2SeqModel.get_batch()

Example

For detailed usage, please see [Adopted] human-motion-prediction-pytorch\src\predict.ipynb

Reminder

The kinect camera’s output is not guaranteed to be consistent with the input of this model (some features are cut off), so further research is needed.

Pykinect

Run pyKinectAzure\examples\exampleBodyTrackingTransformationComparison to get the camera output record in pyKinectAzure\saved_data, saved as .npy