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.
684ae1efdbd21a9cc27b0f19
Yuchen Zhang
684ae1efdbd21a9cc27b0f1a
Nikhil Keetha
684ae1efdbd21a9cc27b0f1b
Chenwei Lyu
684ae1efdbd21a9cc27b0f1c
Bhuvan Jhamb
684ae1efdbd21a9cc27b0f1d
Yutian Chen
claimed_verified
2025-07-13T07:30:01.337Z
65bf190cf629068c7f73ced0
https://cdn-avatars.huggingface.co/v1/production/uploads/65bf190cf629068c7f73ced0/4EcW7AS7HMQITtiUcJM2-.jpeg
Yutian Chen
user
YutianChen
684ae1efdbd21a9cc27b0f1e
Yuheng Qiu
684ae1efdbd21a9cc27b0f1f
Jay Karhade
684ae1efdbd21a9cc27b0f20
Shreyas Jha
684ae1efdbd21a9cc27b0f21
Yaoyu Hu
684ae1efdbd21a9cc27b0f22
Deva Ramanan
684ae1efdbd21a9cc27b0f23
Sebastian Scherer
684ae1efdbd21a9cc27b0f24
Wenshan Wang
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.
A Unified Flow & Matching model (UFM) improves dense image correspondence accuracy and speed by using a transformer architecture for unified data training, outperforming specialized methods for both optical flow and wide-baseline scenarios.
transformer architecture dense image correspondence visual odometry 3D reconstruction object association re-identification wide-baseline scenarios optical flow estimation co-visible pixels (u v) flow coarse-to-fine cost volumes Unimatch RoMa