@misc{zhang2026megaflow,
author = {Zhang, Dingxi and Wang, Fangjinhua and Pollefeys, Marc and Xu, Haofei},
title = {MegaFlow: Zero-Shot Large Displacement Optical Flow},
howpublished = {arXiv},
year = {2026},
month = {March},
abstract = {Accurate estimation of large displacement optical flow remains a critical challenge. Existing methods typically rely on iterative local search or/and domain-specific fine-tuning, which severely limits their performance in large displacement and zero-shot generalization scenarios. To overcome this, we introduce MegaFlow, a simple yet powerful model for zero-shot large displacement optical flow. Rather than relying on highly complex, task-specific architectural designs, MegaFlow adapts powerful pre-trained vision priors to produce temporally consistent motion fields. In particular, we formulate flow estimation as a global matching problem by leveraging pre-trained global Vision Transformer features, which naturally capture large displacements. This is followed by a few lightweight iterative refinements to further improve the sub-pixel accuracy. Extensive experiments demonstrate that MegaFlow achieves state-of-the-art zero-shot performance across multiple optical flow benchmarks. Moreover, our model also delivers highly competitive zero-shot performance on long-range point tracking benchmarks, demonstrating its robust transferability and suggesting a unified paradigm for generalizable motion estimation. Our project page is at: https://kristen-z.github.io/projects/megaflow.},
url = {http://approjects.co.za/?big=en-us/research/publication/megaflow-zero-shot-large-displacement-optical-flow/},
}