Abstract
Despite recent emergence of adversarial based methods
for video prediction, existing algorithms often produce unsatisfied results in image regions with rich structural information (i.e., object boundary) and detailed motion (i.e., articulated body movement). To this end, we present a structure preserving video prediction framework to explicitly address above issues and enhance video prediction quality.
On one hand, our framework contains a two-stream generation architecture which deals with high frequency video
content (i.e., detailed object or articulated motion structure)
and low frequency video content (i.e., location or moving
directions) in two separate streams. On the other hand, we
propose a RNN structure for video prediction, which employs temporal-adaptive convolutional kernels to capture
time-varying motion patterns as well as tiny objects within
a scene. Extensive experiments on diverse scenes, ranging
from human motion to semantic layout prediction, demonstrate the effectiveness of the proposed video prediction approach