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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 anenergy-based spatial-temporal generative ConvNet can be used to model andsynthesize dynamic patterns. The model defines a probability distribution onthe video sequence, and the log probability is defined by a spatial-temporalConvNet that consists of multiple layers of spatial-temporal filters to capturespatial-temporal patterns of different scales. The model can be learned fromthe training video sequences by an "analysis by synthesis" learning algorithmthat iterates the following two steps. Step 1 synthesizes video sequences fromthe currently learned model. Step 2 then updates the model parameters based onthe difference between the synthesized video sequences and the observedtraining sequences. We show that the learning algorithm can synthesizerealistic dynamic patterns. We also show that it is possible to learn the modelfrom incomplete training sequences with either occluded pixels or missingframes, so that model learning and pattern completion can be accomplishedsimultaneously.

Learning Energy-based Spatial-Temporal Generative ConvNets for Dynamic Patterns