UFM: A Simple Path towards Unified Dense Correspondence with Flow
Authors: name: Yuchen Zhang, name: Nikhil Keetha, name: Chenwei Lyu, name: Bhuvan Jhamb, name: Yutian Chen, name: Yuheng Qiu, name: Jay Karhade, name: Shreyas Jha, name: Yaoyu Hu, name: Deva Ramanan, name: Sebastian Scherer, name: Wenshan Wang

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.