  Synthetic datasets play a critical role in pre-training CNN models for
optical flow, but they are painstaking to generate and hard to adapt to new
applications. To automate the process, we present AutoFlow, a simple and
effective method to render training data for optical flow that optimizes the
performance of a model on a target dataset. AutoFlow takes a layered approach
to render synthetic data, where the motion, shape, and appearance of each layer
are controlled by learnable hyperparameters. Experimental results show that
AutoFlow achieves state-of-the-art accuracy in pre-training both PWC-Net and
RAFT. Our code and data are available at https://autoflow-google.github.io .
