DescriptionWe present the first application of Volumetric Generative Adversarial Network (VGAN) to High Energy Physics simulation. We generate three dimensional images of particles depositing energy in calorimeters. This is the first time such an approach is taken in HEP where most of data is three dimensional in nature but it is customary to convert it into two dimensional slices. The volumetric approach leads to a larger number of parameters, but two dimensional slicing loses the volumetric dependencies inherent in the dataset. The present work proves the success of handling those dependencies through VGANs. Energy showers are faithfully reproduced in all dimensions and show a reasonable agreement with standard techniques. We also demonstrate the ability to condition training on several parameters such as particle type and energy. This work aims at proving Deep Learning techniques represent a valid fast alternative to standard MonteCarlo approaches and is part of the GEANTV project.