  In this work we present an adversarial training algorithm that exploits
correlations in video to learn --without supervision-- an image generator model
with a disentangled latent space. The proposed methodology requires only a few
modifications to the standard algorithm of Generative Adversarial Networks
(GAN) and involves training with sets of frames taken from short videos. We
train our model over two datasets of face-centered videos which present
different people speaking or moving the head: VidTIMIT and YouTube Faces
datasets. We found that our proposal allows us to split the generator latent
space into two subspaces. One of them controls content attributes, those that
do not change along short video sequences. For the considered datasets, this is
the identity of the generated face. The other subspace controls motion
attributes, those attributes that are observed to change along short videos. We
observed that these motion attributes are face expressions, head orientation,
lips and eyes movement. The presented experiments provide quantitative and
qualitative evidence supporting that the proposed methodology induces a
disentangling of this two kinds of attributes in the latent space.
