Flow3r: Factored Flow Prediction for Scalable Visual Geometry Learning

Abstract

Current feed-forward 3D/4D reconstruction systems rely on dense geometry and pose supervision -- expensive to obtain at scale and particularly scarce for dynamic real-world scenes. We present Flow3r, a framework that augments visual geometry learning with dense 2D correspondences (`flow') as supervision, enabling scalable training from unlabeled monocular videos. Our key insight is that the flow prediction module should be factored: predicting flow between two images using geometry latents from one and pose latents from the other. This factorization directly guides the learning of both scene geometry and camera motion, and naturally extends to dynamic scenes. In controlled experiments, we show that factored flow prediction outperforms alternative designs and that performance scales consistently with unlabeled data. Integrating factored flow into existing visual geometry architectures and training with ~800K unlabeled videos, Flow3r achieves state-of-the-art results across eight benchmarks spanning static and dynamic scenes, with its largest gains on in-the-wild dynamic videos where labeled data is most scarce.

Gallery

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Dynamic
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Method

Results

Comparisons

Flow3r

VGGT

Pi3

Improving SoTA Models with Unlabeled Data

Here we scale the training of an off-the-shelf large visual geometry network (π³) by leveraging our factored flow prediction strategy with unlabeled dynamic data. We evaluate performance using pose accuracy and reconstruction metrics in four dynamic datasets (Kinetics700, Epic-Kitchens, Sintel and Bonn) and four static datasets (Co3Dv2, Scannet, NRGBD, and 7-scenes). Best, second and third results are highlighted in light red, orange, and yellow, respectively. Flow3r consistently outperforms state-of-the-art methods in both camera pose estimation and scene reconstruction, demonstrating the effectiveness of leveraging large-scale unlabeled videos for visual geometry learning via factored-flow supervision.

Discussion

In this work, we present flow3r and demonstrate that it effectively leverages in-the-wild unlabeled data by introducing factored flow prediction, advancing visual geometry learning beyond existing fully supervised methods. While our approach opens up new possibilities, several challenges remain.

First, flow3r relies on off-the-shelf models to provide pseudo-ground-truth flow supervision, and there can be domains where such 2D prediction fails, limiting the performance upper bound of flow3r. Second, although our factored flow formulation elegantly handles dynamic scenes and enables flow supervision to improve the learning of both camera motion and scene geometry, flow3r may struggle under complex scenes with multiple moving independently components. Finally, our current experiments operate at a moderate scale (~800K video sequences for flow supervision), and scaling to truly large-scale settings (~10-100M videos) presents an exciting but unexplored direction. While this is out of scope for our work due to computational constraints, we envision flow3r's formulation serving as a building block for future large-scale learning methods.

BibTeX

@inproceedings{cong2026flow3r,
    title={Flow3r: Factored Flow Prediction for Scalable Visual Geometry Learning},
    author={Cong, Zhongxiao and Zhao, Qitao and Jeon, Minsik and Tulsiani, Shubham},
    booktitle={CVPR},
    year={2026}
  }

Acknowledgements

We thank the members of the Physical Perception Lab at CMU for their valuable discussions.

This work was supported by an NVIDIA academic grant. This work used Bridges-2 at Pittsburgh Supercomputing Center through allocation CIS250061 from the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program, which is supported by National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296. This work was supported by Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior/Interior Business Center (DOI/IBC) contract number 140D0423C0074. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DOI/IBC, or the U.S. Government.