UFM: A Simple Path towards Unified Dense Correspondence with Flow

Dense image correspondence is central to many applications, such as visual odometry, 3D reconstruction, object association, and re-identification. Historically, dense correspondence has been tackled separately for wide-baseline scenarios and optical flow estimation, despite the common goal of matching content between two images. In this paper, we develop a Unified Flow & Matching model (UFM), which is trained on unified data for pixels that are co-visible in both source and target images. UFM uses a simple, generic transformer architecture that directly regresses the (u,v) flow. It is easier to train and more accurate for large flows compared to the typical coarse-to-fine cost volumes in prior work. UFM is 28% more accurate than state-of-the-art flow methods (Unimatch), while also having 62% less error and 6.7x faster than dense wide-baseline matchers (RoMa). UFM is the first to demonstrate that unified training can outperform specialized approaches across both domains. This result enables fast, general-purpose correspondence and opens new directions for multi-modal, long-range, and real-time correspondence tasks.

@article{zhang2025_2506.09278,
  title={ UFM: A Simple Path towards Unified Dense Correspondence with Flow },
  author={ Yuchen Zhang and Nikhil Keetha and Chenwei Lyu and Bhuvan Jhamb and Yutian Chen and Yuheng Qiu and Jay Karhade and Shreyas Jha and Yaoyu Hu and Deva Ramanan and Sebastian Scherer and Wenshan Wang },
  journal={arXiv preprint arXiv:2506.09278},
  year={ 2025 }
}