Abstract
We present FlowSeek, a novel framework for optical flow requiring minimal hardware resources for training. FlowSeek marries the latest advances on the design space of optical flow networks with cutting-edge single-image depth foundation models and classical low-dimensional motion parametrization, implementing a compact, yet accurate architecture. FlowSeek is trained on a single consumer-grade GPU, a hardware budget about 8x lower compared to most recent methods, and still achieves superior cross-dataset generalization on Sintel Final and KITTI, with a relative improvement of 10 and 15% over the previous state-of-the-art SEA-RAFT, as well as on Spring and LayeredFlow datasets.
Abstract (translated)
我们介绍了FlowSeek，这是一个新颖的光流框架，它在训练时所需的硬件资源非常有限。FlowSeek将最新的光学流网络设计空间进展与最先进的单图像深度基础模型以及经典的低维运动参数化相结合，实现了一个既紧凑又准确的架构。FlowSeek可以在单一消费级GPU上进行训练，其硬件预算比最近大多数方法低约8倍，但在Sintel Final和KITTI数据集上的跨数据集泛化性能仍然优于之前最先进的SEA-RAFT方法，相对改进分别达到10%和15%，并且在Spring和LayeredFlow数据集上也表现出色。
URL
https://arxiv.org/abs/2509.05297
