This paper proposes a network architecture to perform variable length
semantic video generation using captions. We adopt a new perspective towards
video generation where we allow the captions to be combined with the long-term
and short-term dependencies between video frames and thus generate a video in
an incremental manner. Our experiments demonstrate our network architecture's
ability to distinguish between objects, actions and interactions in a video and
combine them to generate videos for unseen captions. The network also exhibits
the capability to perform spatio-temporal style transfer when asked to generate
videos for a sequence of captions. We also show that the network's ability to
learn a latent representation allows it generate videos in an unsupervised
manner and perform other tasks such as action recognition. (Accepted in
International Conference in Computer Vision (ICCV) 2017)

该研究提出了一种网络架构，利用标题执行可变长度的语义视频生成。网络能够区分视频中的对象，动作和交互，并将它们与长期和短期依赖结合起来，以增量方式生成视频。网络通过学习潜在表示对视频进行无监督生成，并在执行其他任务时表现出了良好的能力，如动作识别和空时样式转移。