Delving into the Synthesizability of Dynamic Texture Samples

The example-based dynamic texture synthesis (EDTS) methods have emerged in multitude, dedicated to generating new dynamic textures (DTs) of high quality from an input exemplar. The problem of EDTS has been studied for several decades, but none of the existing synthesis methods are able to tackle all kinds of dynamic textures equally well. Rather than focus on new synthesis methods, we turn to another way to help EDTS by investigating dynamic texture synthesizability - how synthesizable a specific dynamic texture sample is by EDTS. We propose to predict synthesizability score of a given dynamic texture sample, and suggest which EDTS method is best suited to synthesize it. To this end, we compiled a dynamic texture dataset and annotated each DT in terms of synthesizability. We address the problem of learning dynamic texture synthesizability by using regression model to train a predictor on the data collection. More precisely, we first characterize DT samples by a set of spatiotemporal features. Then, based on dynamic texture descriptors, we train regression models to estimate synthesizability scores and use an additional classifier to choose the optimal EDTS methods. The experiments demonstrate that our method can predict the synthesizability of DT samples effectively.