The application of multi-modal generative models by means of a Variational Auto Encoder (VAE) is an upcoming research topic for sensor fusion and bi-directional modality exchange.
 This contribution gives insights into the learned joint latent representation and shows that expressiveness and coherence are decisive properties for multi-modal datasets.
 Furthermore, we propose a multi-modal VAE derived from the full joint marginal log-likelihood that is able to learn the most meaningful representation for ambiguous observations.
 Since the properties of multi-modal sensor setups are essential for our approach but hardly available, we also propose a technique to generate correlated datasets from uni-modal ones.

Deriving a general formulation of a multi-modal VAE from the joint marginal log-likelihood. Proposes a multi-modal VAE with a variational bound derived from the chain rule. This paper proposes an objective, M^2VAE, for multi-modal VAEs, which is supposed to learn a more meaningful latent space representation.