Generative adversarial networks (GANs) have been promise on generating realistic images and hence have been studied widely. Notably, graphical generative adversarial networks (graphical-GAN) introduce Bayesian networks to the GAN framework to learn the underlying structures of data in an unsupervised manner. This study proposes a conditional version of graphical-GAN, which can leverage coarse side information to enhance the graphical-GAN and learn finer and more complex structures, in weakly-supervised learning settings. The inference and learning of conditional graphical-GAN follows a similar protocol to graphical-GAN. Two instances of conditional graphical-GAN are presented. The conditional Gaussian mixture GAN can learn fine clusters from mixture data given a coarse label. The conditional state space GAN can learn the dynamics of videos with multiple objects given the labels of the objects..