MotionZero:Exploiting Motion Priors for Zero-shot Text-to-Video
  Generation
Sitong Su Litao Guo Lianli Gao Hengtao Shen Jingkuan Song
Zero-shot Text-to-Video synthesis generates videos based on prompts without
any videos. Without motion information from videos, motion priors implied in
prompts are vital guidance. For example, the prompt "airplane landing on the
runway" indicates motion priors that the "airplane" moves downwards while the
"runway" stays static. Whereas the motion priors are not fully exploited in
previous approaches, thus leading to two nontrivial issues: 1) the motion
variation pattern remains unaltered and prompt-agnostic for disregarding motion
priors; 2) the motion control of different objects is inaccurate and entangled
without considering the independent motion priors of different objects. To
tackle the two issues, we propose a prompt-adaptive and disentangled motion
control strategy coined as MotionZero, which derives motion priors from prompts
of different objects by Large-Language-Models and accordingly applies motion
control of different objects to corresponding regions in disentanglement.
Furthermore, to facilitate videos with varying degrees of motion amplitude, we
propose a Motion-Aware Attention scheme which adjusts attention among frames by
motion amplitude. Extensive experiments demonstrate that our strategy could
correctly control motion of different objects and support versatile
applications including zero-shot video edit.