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

Dynamic Texture Synthesis by Incorporating Long-range Spatial and Temporal Correlations