  We propose a fully-convolutional conditional generative model, the latent
transformation neural network (LTNN), capable of view synthesis using a
light-weight neural network suited for real-time applications. In contrast to
existing conditional generative models which incorporate conditioning
information via concatenation, we introduce a dedicated network component, the
conditional transformation unit (CTU), designed to learn the latent space
transformations corresponding to specified target views. In addition, a
consistency loss term is defined to guide the network toward learning the
desired latent space mappings, a task-divided decoder is constructed to refine
the quality of generated views, and an adaptive discriminator is introduced to
improve the adversarial training process. The generality of the proposed
methodology is demonstrated on a collection of three diverse tasks: multi-view
reconstruction on real hand depth images, view synthesis of real and synthetic
faces, and the rotation of rigid objects. The proposed model is shown to exceed
state-of-the-art results in each category while simultaneously achieving a
reduction in the computational demand required for inference by 30% on average.
