• Theano
• matplotlib ( for visualization )

Human motion prediction is the task of predicting the future movements of an object given few initial frames. It is a touch problem as movement of humans is highly asynchronous.

In this work we use Graph CNNs to model the motion of several mo-cap features of different body parts i.e. head, torso, arms and the relationships among them. Graph-CNNs provide a more structured way to model the spatio-temporal connections through the adjacency matrix. Here we also add connections through time over vanilla G-CNNs to model the temporal connectivity of the features.

+---code
|    +-- Files to run the model
|    +---neuralmodels
|    |     +--- Contains layers and the main models (some layers borrowed from [NeuralModels](https://github.com/asheshjain399/NeuralModels))
|    +--- utilis
|    |      +--- Basic utility files
+---savedModels
|    +--- Directory automatically created for saving checkpoints and test projections
+---dataset
|    +--- store the provided dataset file here
+---visualize
|    +---scripts to make visualizations as above

The main.py file sets up the whole model and calls other helper files. Learning rates, hidden layer sizes weights initializations etc can be changed using python options. defineGCN.py consists of the architecture of the model, number of layers can be modified from here. trainModel.py calls the fit function of the model and loads the dataset. The main model is in neuralmodels/models/model.py. This file is ideally not to be changed.

Download the dataset as

wget http://www.cs.stanford.edu/people/ashesh/h3.6m.zip
git clone https://github.com/siddsax/Motion_Prediction
cd Motion_Prediction
unzip h3.6m.zip
mv h3.6m/* .
rm -rf h3.6m

Now train a model as

cd code
python -W ignore main.py

This will create projections in the folder savedModels periodically which can then be used as follows to produce mice movies like the following for smoking.

cd ../visualize
python forward_kinematics.py $fileNameToVisualize