# Synthesizing Dynamic Patterns by Spatial-Temporal Generative ConvNet

Jianwen Xie,
Song-Chun Zhu,
and Ying Nian Wu

University of California, Los Angeles (UCLA), USA

## Abstract

Video sequences contain rich dynamic patterns, such as dynamic texture patterns that exhibit stationarity in the temporal
domain, and action patterns that are non-stationary in either spatial or temporal domain. We show that a spatialtemporal
generative ConvNet can be used to model and synthesize dynamic patterns. The model defines a probability
distribution on the video sequence, and the log probability is defined by a spatial-temporal ConvNet that consists of
multiple layers of spatial-temporal filters to capture spatialtemporal patterns of different scales. The model can be
learned from the training video sequences by an “analysis by synthesis” learning algorithm that iterates the following
two steps. Step 1 synthesizes video sequences from the currently learned model. Step 2 then updates the model parameters
based on the difference between the synthesized video sequences and the observed training sequences. We
show that the learning algorithm can synthesize realistic dynamic patterns

## Paper

The paper can be downloaded here.

## Code and Data

The code and data for synthesis experiment can be downloaded here.

The code and data for recovery experiment can be downloaded here.

## Experiments

### Experiment 1: Generating dynamic textures with both spatial and temporal stationarity

In each example, the first one is the observed video, the other three are the synthesized videos.

### Experiment 2: Generating dynamic textures with only temporal stationarity

Exp 2.1: Learning from one training video

In each example, the first one is the observed video, the other three are the synthesized videos.

Exp 2.2: Learning from multiple training videos

The first panel displays 30 observed videos, the second panel displays 39 synthesized videos.

### Experiment 3: Generating action patterns without spatial or temporal stationarity

In the example of running cow (tiger), the first five (two) are the original videos, the rest are the synthesized videos.

### Experiment 4: Learning from incomplete data

In each example, the first one is the ground truth video, the second one is the occluded training video, and the third one is the recovered result.

### Experiment 5: Background inpainting

In each example, the first one is the original video, the second one is the video with black mask occluding the target to be removed, and the third one is the inpainting result by our algorithm.

## Acknowledgement

The work is supported by NSF DMS 1310391, DARPA SIMPLEX N66001-15-C-4035, ONR MURI N00014-16-1-2007, and DARPA ARO W911NF-16-1-0579.