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Video sequences contain rich dynamic patterns, such as dynamic texturepatterns that exhibit stationarity in the temporal domain, and action patternsthat are non-stationary in either spatial or temporal domain. We show that aspatial-temporal generative ConvNet can be used to model and synthesize dynamicpatterns. The model defines a probability distribution on the video sequence,and the log probability is defined by a spatial-temporal ConvNet that consistsof multiple layers of spatial-temporal filters to capture spatial-temporalpatterns of different scales. The model can be learned from the training videosequences by an "analysis by synthesis" learning algorithm that iterates thefollowing two steps. Step 1 synthesizes video sequences from the currentlylearned model. Step 2 then updates the model parameters based on the differencebetween the synthesized video sequences and the observed training sequences. Weshow that the learning algorithm can synthesize realistic dynamic patterns.

Synthesizing Dynamic Patterns by Spatial-Temporal Generative ConvNet