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arXiv · 1610.00527

Video Pixel Networks

Nal Kalchbrenner·Aaron van den Oord·Karen Simonyan·Ivo Danihelka·Oriol Vinyals·Alex Graves·Koray Kavukcuoglu
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Abstract

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.

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Nal Kalchbrenner, Aaron van den Oord, Karen Simonyan, Ivo Danihelka, Oriol Vinyals, Alex Graves, Koray Kavukcuoglu. 2016-10-03. Video Pixel Networks. https://arxiv.org/abs/1610.00527

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