  We introduce optical Flow transFormer, dubbed as FlowFormer, a
transformer-based neural network architecture for learning optical flow.
FlowFormer tokenizes the 4D cost volume built from an image pair, encodes the
cost tokens into a cost memory with alternate-group transformer (AGT) layers in
a novel latent space, and decodes the cost memory via a recurrent transformer
decoder with dynamic positional cost queries. On the Sintel benchmark,
FlowFormer achieves 1.159 and 2.088 average end-point-error (AEPE) on the clean
and final pass, a 16.5% and 15.5% error reduction from the best published
result (1.388 and 2.47). Besides, FlowFormer also achieves strong
generalization performance. Without being trained on Sintel, FlowFormer
achieves 1.01 AEPE on the clean pass of Sintel training set, outperforming the
best published result (1.29) by 21.7%.
