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Recent work on dense optical flow has shown significant progress, primarilyin a supervised learning manner requiring a large amount of labeled data. Dueto the expensiveness of obtaining large scale real-world data, computergraphics are typically leveraged for constructing datasets. However, there is acommon belief that synthetic-to-real domain gaps limit generalization to realscenes. In this paper, we show that the required characteristics in an opticalflow dataset are rather simple and present a simpler synthetic data generationmethod that achieves a certain level of realism with compositions of elementaryoperations. With 2D motion-based datasets, we systematically analyze thesimplest yet critical factors for generating synthetic datasets. Furthermore,we propose a novel method of utilizing occlusion masks in a supervised methodand observe that suppressing gradients on occluded regions serves as a powerfulinitial state in the curriculum learning sense. The RAFT network initiallytrained on our dataset outperforms the original RAFT on the two mostchallenging online benchmarks, MPI Sintel and KITTI 2015.

The Devil in the Details: Simple and Effective Optical Flow Synthetic Data Generation