  The enduring inability of image generative models to recreate intricate
geometric features, such as those present in human hands and fingers has been
an ongoing problem in image generation for nearly a decade. While strides have
been made by increasing model sizes and diversifying training datasets, this
issue remains prevalent across all models, from denoising diffusion models to
Generative Adversarial Networks (GAN), pointing to a fundamental shortcoming in
the underlying architectures. In this paper, we demonstrate how this problem
can be mitigated by augmenting convolution layers geometric capabilities
through providing them with a single input channel incorporating the relative
$n$-dimensional Cartesian coordinate system. We show that this drastically
improves quality of hand and face images generated by GANs and Variational
AutoEncoders (VAE).
