  Generating videos with content and motion variations is a challenging task in
computer vision. While the recent development of GAN allows video generation
from latent representations, it is not easy to produce videos with particular
content of motion patterns of interest. In this paper, we propose Dual Motion
Transfer GAN (Dual-MTGAN), which takes image and video data as inputs while
learning disentangled content and motion representations. Our Dual-MTGAN is
able to perform deterministic motion transfer and stochastic motion generation.
Based on a given image, the former preserves the input content and transfers
motion patterns observed from another video sequence, and the latter directly
produces videos with plausible yet diverse motion patterns based on the input
image. The proposed model is trained in an end-to-end manner, without the need
to utilize pre-defined motion features like pose or facial landmarks. Our
quantitative and qualitative results would confirm the effectiveness and
robustness of our model in addressing such conditioned image-to-video tasks.
