  Visual motion processing is essential for humans to perceive and interact
with dynamic environments. Despite extensive research in cognitive
neuroscience, image-computable models that can extract informative motion flow
from natural scenes in a manner consistent with human visual processing have
yet to be established. Meanwhile, recent advancements in computer vision (CV),
propelled by deep learning, have led to significant progress in optical flow
estimation, a task closely related to motion perception. Here we propose an
image-computable model of human motion perception by bridging the gap between
biological and CV models. Specifically, we introduce a novel two-stages
approach that combines trainable motion energy sensing with a recurrent
self-attention network for adaptive motion integration and segregation. This
model architecture aims to capture the computations in V1-MT, the core
structure for motion perception in the biological visual system, while
providing the ability to derive informative motion flow for a wide range of
stimuli, including complex natural scenes. In silico neurophysiology reveals
that our model's unit responses are similar to mammalian neural recordings
regarding motion pooling and speed tuning. The proposed model can also
replicate human responses to a range of stimuli examined in past psychophysical
studies. The experimental results on the Sintel benchmark demonstrate that our
model predicts human responses better than the ground truth, whereas the
state-of-the-art CV models show the opposite. Our study provides a
computational architecture consistent with human visual motion processing,
although the physiological correspondence may not be exact.
