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Perception is often described as a predictive process based on an optimalinference with respect to a generative model. We study here the principledconstruction of a generative model specifically crafted to probe motionperception. In that context, we first provide an axiomatic, biologically-drivenderivation of the model. This model synthesizes random dynamic textures whichare defined by stationary Gaussian distributions obtained by the randomaggregation of warped patterns. Importantly, we show that this model canequivalently be described as a stochastic partial differential equation. Usingthis characterization of motion in images, it allows us to recast motion-energymodels into a principled Bayesian inference framework. Finally, we apply thesetextures in order to psychophysically probe speed perception in humans. In thisframework, while the likelihood is derived from the generative model, the prioris estimated from the observed results and accounts for the perceptual bias ina principled fashion.

Biologically Inspired Dynamic Textures for Probing Motion Perception