  Conditional domain generation is a good way to interactively control sample
generation process of deep generative models. However, once a conditional
generative model has been created, it is often expensive to allow it to adapt
to new conditional controls, especially the network structure is relatively
deep. We propose a conditioned latent domain transfer framework across latent
spaces of unconditional variational autoencoders(VAE). With this framework, we
can allow unconditionally trained VAEs to generate images in its domain with
conditionals provided by a latent representation of another domain. This
framework does not assume commonalities between two domains. We demonstrate
effectiveness and robustness of our model under widely used image datasets.
