OCAI: Improving Optical Flow Estimation by Occlusion and Consistency
  Aware Interpolation
Jisoo Jeong Hong Cai Risheek Garrepalli Jamie Menjay Lin Munawar Hayat Fatih Porikli
The scarcity of ground-truth labels poses one major challenge in developing
optical flow estimation models that are both generalizable and robust. While
current methods rely on data augmentation, they have yet to fully exploit the
rich information available in labeled video sequences. We propose OCAI, a
method that supports robust frame interpolation by generating intermediate
video frames alongside optical flows in between. Utilizing a forward warping
approach, OCAI employs occlusion awareness to resolve ambiguities in pixel
values and fills in missing values by leveraging the forward-backward
consistency of optical flows. Additionally, we introduce a teacher-student
style semi-supervised learning method on top of the interpolated frames. Using
a pair of unlabeled frames and the teacher model's predicted optical flow, we
generate interpolated frames and flows to train a student model. The teacher's
weights are maintained using Exponential Moving Averaging of the student. Our
evaluations demonstrate perceptually superior interpolation quality and
enhanced optical flow accuracy on established benchmarks such as Sintel and
KITTI.