This paper studies the dynamic generator model for spatial-temporal processes
such as dynamic textures and action sequences in video data. In this model,
each time frame of the video sequence is generated by a generator model, which
is a non-linear transformation of a latent state vector, where the non-linear
transformation is parametrized by a top-down neural network. The sequence of
latent state vectors follows a non-linear auto-regressive model, where the
state vector of the next frame is a non-linear transformation of the state
vector of the current frame as well as an independent noise vector that
provides randomness in the transition. The non-linear transformation of this
transition model can be parametrized by a feedforward neural network. We show
that this model can be learned by an alternating back-propagation through time
algorithm that iteratively samples the noise vectors and updates the parameters
in the transition model and the generator model. We show that our training
method can learn realistic models for dynamic textures and action patterns.

本研究研究了动态生成模型的空间 - 时间过程，如视频数据中的动态纹理和动作序列，模型通过一个生成模型产生视频序列中的每个时间帧，生成模型是潜在状态向量的非线性转换，并由自回归模型控制。