  We introduce a two-stream model for dynamic texture synthesis. Our model is
based on pre-trained convolutional networks (ConvNets) that target two
independent tasks: (i) object recognition, and (ii) optical flow prediction.
Given an input dynamic texture, statistics of filter responses from the object
recognition ConvNet encapsulate the per-frame appearance of the input texture,
while statistics of filter responses from the optical flow ConvNet model its
dynamics. To generate a novel texture, a randomly initialized input sequence is
optimized to match the feature statistics from each stream of an example
texture. Inspired by recent work on image style transfer and enabled by the
two-stream model, we also apply the synthesis approach to combine the texture
appearance from one texture with the dynamics of another to generate entirely
novel dynamic textures. We show that our approach generates novel, high quality
samples that match both the framewise appearance and temporal evolution of
input texture. Finally, we quantitatively evaluate our texture synthesis
approach with a thorough user study.
