  Recent advances in high-fidelity semantic image editing heavily rely on the
presumably disentangled latent spaces of the state-of-the-art generative
models, such as StyleGAN. Specifically, recent works show that it is possible
to achieve decent controllability of attributes in face images via linear
shifts along with latent directions. Several recent methods address the
discovery of such directions, implicitly assuming that the state-of-the-art
GANs learn the latent spaces with inherently linearly separable attribute
distributions and semantic vector arithmetic properties.
  In our work, we show that nonlinear latent code manipulations realized as
flows of a trainable Neural ODE are beneficial for many practical non-face
image domains with more complex non-textured factors of variation. In
particular, we investigate a large number of datasets with known attributes and
demonstrate that certain attribute manipulations are challenging to obtain with
linear shifts only.
