  Being able to generate constrained samples is one of the most appealing
applications of the deep generators. Conditional generators are one of the
successful implementations of such models wherein the created samples are
constrained to a specific class. In this work, the application of these
networks is extended to regression problems wherein the conditional generator
is restrained to any continuous aspect of the data. A new loss function is
presented for the regression network and also implementations for generating
faces with any particular set of landmarks is provided.
