  Perception is often described as a predictive process based on an optimal
inference with respect to a generative model. We study here the principled
construction of a generative model specifically crafted to probe motion
perception. In that context, we first provide an axiomatic, biologically-driven
derivation of the model. This model synthesizes random dynamic textures which
are defined by stationary Gaussian distributions obtained by the random
aggregation of warped patterns. Importantly, we show that this model can
equivalently be described as a stochastic partial differential equation. Using
this characterization of motion in images, it allows us to recast motion-energy
models into a principled Bayesian inference framework. Finally, we apply these
textures in order to psychophysically probe speed perception in humans. In this
framework, while the likelihood is derived from the generative model, the prior
is estimated from the observed results and accounts for the perceptual bias in
a principled fashion.
