FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases

Matteo Poggi, Fabio Tosi
9/5/2025
cs.CV

No official code found. Best implementation: mattpoggi/flowseek (150 stars, Apache-2.0, last commit 2026-01-13).

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.

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Code Implementations(1)

mattpoggi/flowseekCommunity72%
Apache-2.0

Source code for ICCV 2025 paper "FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases"

15011Shell, PythonSep 5, 20259 months agoApache-2.0

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Cite this paper

@article{poggi2025flowseek,
  title  = {FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases},
  author = {Matteo Poggi and Fabio Tosi},
  year   = {2025},
  eprint = {2509.05297},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2509.05297v1}
}

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