  Optical flow is the motion of a pixel between at least two consecutive video
frames and can be estimated through an end-to-end trainable convolutional
neural network. To this end, large training datasets are required to improve
the accuracy of optical flow estimation. Our paper presents OmniFlow: a new
synthetic omnidirectional human optical flow dataset. Based on a rendering
engine we create a naturalistic 3D indoor environment with textured rooms,
characters, actions, objects, illumination and motion blur where all components
of the environment are shuffled during the data capturing process. The
simulation has as output rendered images of household activities and the
corresponding forward and backward optical flow. To verify the data for
training volumetric correspondence networks for optical flow estimation we
train different subsets of the data and test on OmniFlow with and without
Test-Time-Augmentation. As a result we have generated 23,653 image pairs and
corresponding forward and backward optical flow. Our dataset can be downloaded
from: https://mytuc.org/byfs
