Deep Generative Models: Deterministic Prediction with an Application in Inverse Rendering
This work addresses uncertainty control in deep generative models for inverse rendering, which is incremental as it builds on existing CVAE methods.
The paper tackled the problem of controlling uncertainty in Conditional Variational Autoencoder (CVAE) predictions for 3D shape inverse rendering, showing that more informative conditions about object pose lead to less diverse predictions, with experimental validation on Modelnet10 and Shapenet datasets.
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}".