  Deep Convolutional Neural Networks (CNNs) are powerful models that have
achieved excellent performance on difficult computer vision tasks. Although
CNNs perform well whenever large labeled training samples are available, they
work badly on video frame synthesis due to objects deforming and moving, scene
lighting changes, and cameras moving in video sequence. In this paper, we
present a novel and general end-to-end architecture, called convolutional
Transformer or ConvTransformer, for video frame sequence learning and video
frame synthesis. The core ingredient of ConvTransformer is the proposed
attention layer, i.e., multi-head convolutional self-attention layer, that
learns the sequential dependence of video sequence. ConvTransformer uses an
encoder, built upon multi-head convolutional self-attention layer, to encode
the sequential dependence between the input frames, and then a decoder decodes
the long-term dependence between the target synthesized frames and the input
frames. Experiments on video future frame extrapolation task show
ConvTransformer to be superior in quality while being more parallelizable to
recent approaches built upon convolutional LSTM (ConvLSTM). To the best of our
knowledge, this is the first time that ConvTransformer architecture is proposed
and applied to video frame synthesis.
