Four-dimensional computed tomography (4DCT) acquisitions are used routinely in lung cancer radiotherapy treatment planning to identify target volumes and safety margins. However, they expose the patient to higher radiation dose compared to static 3DCT acquisitions. In this work, we demonstrate the possibility of generating synthetic 4DCT acquisitions from a 3DCT image following the actual patient's respiratory amplitude. To this end, we propose a new image-to-image generative adversarial network (GAN) architecture. More specifically, we propose a new scalar injection mechanism based on Adaptive Instance Normalization to condition the generator on the breathing amplitude. Such information can be obtained in practice using external respiratory tracking devices. We show preliminary results on a series of 4DCT images where we compare our synthesized 4DCT to real respiratory phase-gated acquisitions, paving the way for 4DCT-free treatment planning.