Title: Towards Metamerism via Foveated Style Transfer,
Abstract: The problem of $\textit{visual metamerism}$ is defined as finding a family of
perceptually indistinguishable, yet physically different images. In this paper,
we propose our NeuroFovea metamer model, a foveated generative model that is
based on a mixture of peripheral representations and style transfer
forward-pass algorithms. Our gradient-descent free model is parametrized by a
foveated VGG19 encoder-decoder which allows us to encode images in high
dimensional space and interpolate between the content and texture information
with adaptive instance normalization anywhere in the visual field. Our
contributions include: 1) A framework for computing metamers that resembles a
noisy communication system via a foveated feed-forward encoder-decoder network
-- We observe that metamerism arises as a byproduct of noisy perturbations that
partially lie in the perceptual null space; 2) A perceptual optimization scheme
as a solution to the hyperparametric nature of our metamer model that requires
tuning of the image-texture tradeoff coefficients everywhere in the visual
field which are a consequence of internal noise; 3) An ABX psychophysical
evaluation of our metamers where we also find that the rate of growth of the
receptive fields in our model match V1 for reference metamers and V2 between
synthesized samples. Our model also renders metamers at roughly a second,
presenting a $\times1000$ speed-up compared to the previous work, which allows
for tractable data-driven metamer experiments.