Deep Generative Models such as Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE) have found multiple applications in Robotics, with recent works suggesting the potential use of these methods as a generic solution for the estimation of sampling distributions for motion planning in parameterized sets of environments. In this work we provide a first empirical study of challenges, benefits and drawbacks of utilizing vanilla GANs and VAEs for the approximation of probability distributions arising from sampling-based motion planner path solutions. We present an evaluation on a sequence of simulated 2D configuration spaces of increasing complexity and a 4D planar robot arm scenario and find that vanilla GANs and VAEs both outperform classical statistical estimation by an n-dimensional histogram in our chosen scenarios. We furthermore highlight differences in convergence and noisiness between the trained models and propose and study a benchmark sequence of planar C-space environments parameterized by opened or closed doors. In this setting, we find that the chosen geometrical embedding of the parameters of the family of considered C-spaces is a key performance contributor that relies heavily on human intuition about C-space structure at present. We discuss some of the challenges of parameter selection and convergence for applying this approach with an out-of-the box GAN and VAE model.