  We observe that despite their hierarchical convolutional nature, the
synthesis process of typical generative adversarial networks depends on
absolute pixel coordinates in an unhealthy manner. This manifests itself as,
e.g., detail appearing to be glued to image coordinates instead of the surfaces
of depicted objects. We trace the root cause to careless signal processing that
causes aliasing in the generator network. Interpreting all signals in the
network as continuous, we derive generally applicable, small architectural
changes that guarantee that unwanted information cannot leak into the
hierarchical synthesis process. The resulting networks match the FID of
StyleGAN2 but differ dramatically in their internal representations, and they
are fully equivariant to translation and rotation even at subpixel scales. Our
results pave the way for generative models better suited for video and
animation.
