  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.
