Having dense and regularly sampled data is becoming increasingly important in seismic processing. However, due to physical or financial constraints, seismic data sets can be often undersampled. Occasionally, these data sets may also present bad or dead traces the geoscientist must deal with. Many works have tackled this problem using prestack data and can be classified in three main categories: wave-equation, domain transform, and prediction-error-filter methods. In this letter, we assess the performance of a conditional generative adversarial network for the interpolation problem in poststack seismic data sets. To the best of our knowledge, this is the first work to evaluate a deep learning approach in this context. Quantitative and qualitative evaluations of our experiments indicate that deep networks may present an interesting alternative to classical methods.