Torch implementation for Unsupervised Learning of Disentangled Representations from Video.
To train the base model run:
th train_drnet.lua
or the model with skip connections between content encoder and decoder:
th train_drnet_skip.lua
To train an LSTM on the pose vectors run:
th train_lstm.lua --modelPath /path/to/model/
First download the KTH action recognition dataset by running:
sh datasets/download_kth.sh /my/kth/data/path/
where /my/kth/data/path/ is the directory the data will be downloaded into. Next, convert the downloaded .avi files into .png's for the data loader. To do this you'll want ffmpeg installed. Then run:
th datasets/convert_kth.lua --dataRoot /my/kth/data/path/ --imageSize 128
The --imageSize flag specifiec the image resolution. Experimental results in the paper used 128x128, but you can also train a model on 64x64 and it will train much faster.
Now you're ready to train the DrNet model by running:
th train_drnet_skip.lua --dataRoot /my/kth/data/path/ --imageSize 128 --nThreads 2
Setting --nThreads utilizes multithreaded data loading and will speed up training significantly.