Two-Stream Convolutional Networks for Dynamic Texture Synthesis

Understand

We introduce a two-stream model for dynamic texture synthesis.

• Our model is based on pre-trained convolutional networks (ConvNets) that target two independent tasks: (i) object recognition, and (ii) optical flow prediction.
• Given an input dynamic texture, statistics of filter responses from the object recognition ConvNet encapsulate the per-frame appearance of the input texture, while statistics of filter responses from the optical flow ConvNet model its dynamics.
• To generate a novel texture, a randomly initialized input sequence is optimized to match the feature statistics from each stream of an example texture.