Title: Towards Metamerism via Foveated Style Transfer. Abstract: The problem of 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 ×1000 speed-up compared to the previous work, which now
allows for tractable data-driven metamer experiments.