Video Pixel Networks
  We propose a probabilistic video model, the Video Pixel Network (VPN), that estimates the discrete joint distribution of the raw pixel values in a video. The model and the neural architecture reflect the time, space and color structure of video tensors and encode it as a four-dimensional dependency chain. The VPN approaches the best possible performance on the Moving MNIST benchmark, a leap over the previous state of the art, and the generated videos show only minor deviations from the ground truth. The VPN also produces detailed samples on the action-conditional Robotic Pushing benchmark and generalizes to the motion of novel objects. 
Nal Kalchbrenner, Aaron van den Oord, Karen Simonyan, Ivo Danihelka,   Oriol Vinyals, Alex Graves, Koray Kavukcuoglu
cs.CV cs.LG