This paper presents a method named 3D-GAN, based on a generative adversarial network (GAN), to retrieve the total mass, 3D structure and the internal mass distribution of snowflakes. The method uses as input a triplet of binary silhouettes of particles, corresponding to the triplet of stereoscopic images of snowflakes in free fall captured by a multi-angle snowflake camera (MASC). The 3D-GAN method is trained on simulated snowflakes of known characteristics whose silhouettes are statistically similar to real MASC observations, and it is evaluated by means of snowflake replicas printed in 3D at 1 : 1 scale. The estimation of mass obtained by 3D-GAN has a normalized RMSE (NRMSE) of 40 %, a mean normalized bias (MNB) of 8% and largely outperforms standard relationships based on maximum size and compactness. The volume of the convex hull of the particles is retrieved with NRMSE of 35% and MNB of +19 %. In order to illustrate the potential of 3D-GAN to study snowfall microphysics and highlight its complementarity with existing retrieval algorithms, some application examples and ideas are provided, using as showcases the large available datasets of MASC images collected worldwide during various field campaigns. The combination of mass estimates (from 3D-GAN) and hydrometeor classification or riming degree estimation (from independent methods) allows, for example, to obtain mass-to-size power law parameters stratified on hydrometeor type or riming degree. The parameters obtained in this way are consistent with previous findings, with exponents overall around 2 and increasing with the degree of riming.