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.
We introduce a novel feed-forward framework to generate visual metamers Proposes a NeuroFovea model for generation of point-of-fixation metamers by using a style transfer approach via and Encoder-Decoder style architecture An analysis of metamerism and a model capable of rapidly producing metamers of value for experimental psychophysics and other domains. The paper proposes a fast method for generating visual metamers – physically different images that cannot be told apart from an original – via foveated, fast, arbitrary style transfer