  Videos of actions are complex signals containing rich compositional structure
in space and time. Current video generation methods lack the ability to
condition the generation on multiple coordinated and potentially simultaneous
timed actions. To address this challenge, we propose to represent the actions
in a graph structure called Action Graph and present the new ``Action Graph To
Video'' synthesis task. Our generative model for this task (AG2Vid)
disentangles motion and appearance features, and by incorporating a scheduling
mechanism for actions facilitates a timely and coordinated video generation. We
train and evaluate AG2Vid on the CATER and Something-Something V2 datasets, and
show that the resulting videos have better visual quality and semantic
consistency compared to baselines. Finally, our model demonstrates zero-shot
abilities by synthesizing novel compositions of the learned actions. For code
and pretrained models, see the project page https://roeiherz.github.io/AG2Video
