UFM: A Simple Path towards Unified Dense Correspondence with Flow

• URL: http://arxiv.org/abs/2506.09278v1
• Date: Tue, 10 Jun 2025 22:32:13 GMT
• Title: UFM: A Simple Path towards Unified Dense Correspondence with Flow
• Authors: Yuchen Zhang, Nikhil Keetha, Chenwei Lyu, Bhuvan Jhamb, Yutian Chen, Yuheng Qiu, Jay Karhade, Shreyas Jha, Yaoyu Hu, Deva Ramanan, Sebastian Scherer, Wenshan Wang,
• Abstract summary: Unified Flow & Matching model (UFM) is trained on unified data for pixels that are co-visible in both source and target images.<n>UFM is 28% more accurate than state-of-the-art flow methods.
• Score: 40.97394594672024
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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