  Deep generative models like StyleGAN hold the promise of semantic image
editing: modifying images by their content, rather than their pixel values.
Unfortunately, working with arbitrary images requires inverting the StyleGAN
generator, which has remained challenging so far. Existing inversion approaches
obtain promising yet imperfect results, having to trade-off between
reconstruction quality and downstream editability. To improve quality, these
approaches must resort to various techniques that extend the model latent space
after training. Taking a step back, we observe that these methods essentially
all propose, in one way or another, to increase the number of free parameters.
This suggests that inversion might be difficult because it is underconstrained.
In this work, we address this directly and dramatically overparameterize the
latent space, before training, with simple changes to the original StyleGAN
architecture. Our overparameterization increases the available degrees of
freedom, which in turn facilitates inversion. We show that this allows us to
obtain near-perfect image reconstruction without the need for encoders nor for
altering the latent space after training. Our approach also retains
editability, which we demonstrate by realistically interpolating between
images.
