Reviews: Unsupervised Learning of Disentangled Representations from Video

A decoder CNN is then used to combine the content features of one frame and pose features of a different frame to reconstruct the frame that corresponds to the pose features. This paper presents a new loss for the disentanglement of pose features from input images using a discriminator network (similar to adversarial loss). They train a discriminator network find similarities between features from the pose encoder of images from the same video but different time steps. The pose encoder is then trained to find features that the discriminator cannot distinguish as being similar. At convergence, the pose encoder features will only contain difference between the frames which should reflect the pose change of the person in time. After the encoder-decoder CNN has been trained to identify the content and pose features, an LSTM is trained to take the content features of the last frame and previous pose features to predict the next pose features. The disentangled content and pose features result in high quality video prediction. The experimental results backup the advantage of this method in terms of feature quality for classification tasks, and shows quantitative and qualitative performance boost over the state-of-the-art in video prediction.