  The main challenge of dynamic texture synthesis lies in how to maintain
spatial and temporal consistency in synthesized videos. The major drawback of
existing dynamic texture synthesis models comes from poor treatment of the
long-range texture correlation and motion information. To address this problem,
we incorporate a new loss term, called the Shifted Gram loss, to capture the
structural and long-range correlation of the reference texture video.
Furthermore, we introduce a frame sampling strategy to exploit long-period
motion across multiple frames. With these two new techniques, the application
scope of existing texture synthesis models can be extended. That is, they can
synthesize not only homogeneous but also structured dynamic texture patterns.
Thorough experimental results are provided to demonstrate that our proposed
dynamic texture synthesis model offers state-of-the-art visual performance.
