  Image-to-image (i2i) translation is the dense regression problem of learning
how to transform an input image into an output using aligned image pairs.
Remarkable progress has been made in i2i translation with the advent of Deep
Convolutional Neural Networks (DCNNs) and particular using the learning
paradigm of Generative Adversarial Networks (GANs). In the absence of paired
images, i2i translation is tackled with one or multiple domain transformations
(i.e., CycleGAN, StarGAN etc.). In this paper, we study a new problem, that of
image-to-image translation, under a set of continuous parameters that
correspond to a model describing a physical process. In particular, we propose
the SliderGAN which transforms an input face image into a new one according to
the continuous values of a statistical blendshape model of facial motion. We
show that it is possible to edit a facial image according to expression and
speech blendshapes, using sliders that control the continuous values of the
blendshape model. This provides much more flexibility in various tasks,
including but not limited to face editing, expression transfer and face
neutralisation, comparing to models based on discrete expressions or action
units.
