The identification of fetal-head standard planes (FHSPs) from ultrasound (US) images is of fundamental importance to visualize cerebral structures and diagnose neural anomalies during gestation in a standardized way. To support the activity of healthcare operators, deep-learning algorithms have been proposed to classify these planes. To date, the translation of such algorithms in clinical practice is hampered by several factors, including the lack of large annotated datasets to train robust and generalizable algorithms. This paper proposes an approach to generate synthetic FHSP images with conditional generative adversarial network (cGAN), using class activation maps (CAMs) obtained from FHSP classification algorithms as cGAN conditional prior. Using the largest publicly available FHSP dataset, we generated realistic images of the three common FHSPs: trans-cerebellum, trans-thalamic and trans-ventricular. The evaluation through t-SNE shows the potential of the proposed approach to attenuate the problem of limited availability of annotated FHSP images.