  Deep generative models are stochastic neural networks capable of learning the
distribution of data so as to generate new samples. Conditional Variational
Autoencoder (CVAE) is a powerful deep generative model aiming at maximizing the
lower bound of training data log-likelihood. In the CVAE structure, there is
appropriate regularizer, which makes it applicable for suitably constraining
the solution space in solving ill-posed problems and providing high
generalization power. Considering the stochastic prediction characteristic in
CVAE, depending on the problem at hand, it is desirable to be able to control
the uncertainty in CVAE predictions. Therefore, in this paper we analyze the
impact of CVAE's condition on the diversity of solutions given by our designed
CVAE in 3D shape inverse rendering as a prediction problem. The experimental
results using Modelnet10 and Shapenet datasets show the appropriate performance
of our designed CVAE and verify the hypothesis: \emph{"The more informative the
conditions in terms of object pose are, the less diverse the CVAE predictions
are}".
