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.
Denton et al. REF represent videos as stationary and temporally varying components for the prediction of future frames.
Unsupervised Learning of Disentangled Representations from Video
ArXiv
ArXiv
Computer Science Mathematics