  Optical Flow Estimation aims to find the 2D dense motion field between two
frames. Due to the limitation of model structures and training datasets,
existing methods often rely too much on local clues and ignore the integrity of
objects, resulting in fragmented motion estimation. Through theoretical
analysis, we find the pre-trained large vision models are helpful in optical
flow estimation, and we notice that the recently famous Segment Anything Model
(SAM) demonstrates a strong ability to segment complete objects, which is
suitable for solving the fragmentation problem. We thus propose a solution to
embed the frozen SAM image encoder into FlowFormer to enhance object
perception. To address the challenge of in-depth utilizing SAM in
non-segmentation tasks like optical flow estimation, we propose an Optical Flow
Task-Specific Adaption scheme, including a Context Fusion Module to fuse the
SAM encoder with the optical flow context encoder, and a Context Adaption
Module to adapt the SAM features for optical flow task with Learned
Task-Specific Embedding. Our proposed SAMFlow model reaches 0.86/2.10
clean/final EPE and 3.55/12.32 EPE/F1-all on Sintel and KITTI-15 training set,
surpassing Flowformer by 8.5%/9.9% and 13.2%/16.3%. Furthermore, our model
achieves state-of-the-art performance on the Sintel and KITTI-15 benchmarks,
ranking #1 among all two-frame methods on Sintel clean pass.
