Title: Unsupervised Learning of Disentangled Representations from Video,
Abstract: We present a new model DrNET that learns disentangled image representations
from video. Our approach leverages the temporal coherence of video and a novel
adversarial loss to learn a representation that factorizes each frame into a
stationary part and a temporally varying component. The disentangled
representation can be used for a range of tasks. For example, applying a
standard LSTM to the time-vary components enables prediction of future frames.
We evaluate our approach on a range of synthetic and real videos, demonstrating
the ability to coherently generate hundreds of steps into the future.