  We propose a generative framework which takes on the video frame
interpolation problem. Our framework, which we call Deep Locally Linear
Embedding (DeepLLE), is powered by a deep convolutional neural network (CNN)
while it can be used instantly like conventional models. DeepLLE fits an
auto-encoding CNN to a set of several consecutive frames and embeds a linearity
constraint on the latent codes so that new frames can be generated by
interpolating new latent codes. Different from the current deep learning
paradigm which requires training on large datasets, DeepLLE works in a
plug-and-play and unsupervised manner, and is able to generate an arbitrary
number of frames. Thorough experiments demonstrate that without bells and
whistles, our method is highly competitive among current state-of-the-art
models.
