Abstract. Due to the emergence of Generative Adversarial Networks,
video synthesis has witnessed exceptional breakthroughs. However, existing methods lack a proper representation to explicitly control the dynamics in videos. Human pose, on the other hand, can represent motion
patterns intrinsically and interpretably, and impose the geometric constraints regardless of appearance. In this paper, we propose a pose guided
method to synthesize human videos in a disentangled way: plausible motion prediction and coherent appearance generation. In the first stage, a
Pose Sequence Generative Adversarial Network (PSGAN) learns in an
adversarial manner to yield pose sequences conditioned on the class label.
In the second stage, a Semantic Consistent Generative Adversarial Network (SCGAN) generates video frames from the poses while preserving
coherent appearances in the input image. By enforcing semantic consistency between the generated and ground-truth poses at a high feature
level, our SCGAN is robust to noisy or abnormal poses. Extensive experiments on both human action and human face datasets manifest the
superiority of the proposed method over other state-of-the-arts