  Advances in the realm of Generative Adversarial Networks (GANs) have led to
architectures capable of producing amazingly realistic images such as
StyleGAN2, which, when trained on the FFHQ dataset, generates images of human
faces from random vectors in a lower-dimensional latent space. Unfortunately,
this space is entangled - translating a latent vector along its axes does not
correspond to a meaningful transformation in the output space (e.g., smiling
mouth, squinting eyes). The model behaves as a black box, providing neither
control over its output nor insight into the structures it has learned from the
data. We present a method to explore the manifolds of changes of spatially
localized regions of the face. Our method discovers smoothly varying sequences
of latent vectors along these manifolds suitable for creating animations.
Unlike existing disentanglement methods that either require labelled data or
explicitly alter internal model parameters, our method is an optimization-based
approach guided by a custom loss function and manually defined region of
change. Our code is open-sourced, which can be found, along with supplementary
results, on our project page: https://github.com/bmolab/masked-gan-manifold
