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: this https URL.
Abstract (translated)
大位移光流的精确估计仍是一项关键挑战。现有方法通常依赖迭代局部搜索和/或领域特定的微调，这严重限制了其在大位移和零样本泛化场景下的性能。为克服此问题，我们提出了MegaFlow——一个简单而强大的零样本大位移光流模型。MegaFlow不依赖高度复杂、任务特定的架构设计，而是通过适配强大的预训练视觉先验来生成时序一致的运动场。具体而言，我们利用预训练的全局视觉Transformer特征将光流估计建模为全局匹配问题，这些特征天然能够捕捉大位移；随后通过少量轻量级迭代优化进一步提升亚像素精度。大量实验表明，MegaFlow在多个光流基准测试中达到了最先进的零样本性能。此外，我们的模型在长程点跟踪基准测试中也展现出极具竞争力的零样本表现，体现了其强大的迁移能力，并暗示了可泛化运动估计的统一范式。项目页面地址：此https链接。
URL
https://arxiv.org/abs/2603.25739
