  This paper introduces a novel transformer-based network architecture,
FlowFormer, along with the Masked Cost Volume AutoEncoding (MCVA) for
pretraining it to tackle the problem of optical flow estimation. FlowFormer
tokenizes the 4D cost-volume built from the source-target image pair and
iteratively refines flow estimation with a cost-volume encoder-decoder
architecture. The cost-volume encoder derives a cost memory with
alternate-group transformer~(AGT) layers in a latent space and the decoder
recurrently decodes flow from the cost memory with dynamic positional cost
queries. On the Sintel benchmark, FlowFormer architecture achieves 1.16 and
2.09 average end-point-error~(AEPE) on the clean and final pass, a 16.5\% and
15.5\% error reduction from the GMA~(1.388 and 2.47). MCVA enhances FlowFormer
by pretraining the cost-volume encoder with a masked autoencoding scheme, which
further unleashes the capability of FlowFormer with unlabeled data. This is
especially critical in optical flow estimation because ground truth flows are
more expensive to acquire than labels in other vision tasks. MCVA improves
FlowFormer all-sided and FlowFormer+MCVA ranks 1st among all published methods
on both Sintel and KITTI-2015 benchmarks and achieves the best generalization
performance. Specifically, FlowFormer+MCVA achieves 1.07 and 1.94 AEPE on the
Sintel benchmark, leading to 7.76\% and 7.18\% error reductions from
FlowFormer.
