FMPose3D: Monocular 3D Pose Estimation via Flow Matching

Official model weights for FMPose3D: monocular 3D pose estimation via flow matching by Ti Wang, Xiaohang Yu, and Mackenzie Weygandt Mathis.

| Paper | Project Page | GitHub | PyPI |

Overview

FMPose3D lifts 2D keypoints from a single image into 3D poses using flow matching — a generative technique based on ODE sampling. It generates multiple plausible 3D pose hypotheses in just a few steps, then aggregates them using a reprojection-based Bayesian module (RPEA) for accurate predictions, achieving state-of-the-art results on human and animal 3D pose benchmarks.

Available Checkpoints

Filename	Skeleton	Joints	Training Data	Description
fmpose3d_humans.pth	H36M	17	Human3.6M	Human 3D pose estimation
fmpose3d_animals.pth	Animal3D	26	Animal3D	Quadruped animal 3D pose estimation

Quick Start

pip install fmpose3d

Weights are downloaded automatically when using the Python API:

from fmpose3d import FMPose3DInference

# Human — weights auto-downloaded on first use
result = FMPose3DInference().predict("photo.jpg")

# Animal
result = FMPose3DInference.for_animals().predict("dog.jpg")

For the full API reference, see the GitHub repository.

Manual Download

from huggingface_hub import hf_hub_download

path = hf_hub_download(repo_id="DeepLabCut/FMPose3D", filename="fmpose3d_humans.pth")
# or
path = hf_hub_download(repo_id="DeepLabCut/FMPose3D", filename="fmpose3d_animals.pth")

api = FMPose3DInference(model_weights_path=path)

Citation

@misc{wang2026fmpose3dmonocular3dpose,
      title={FMPose3D: monocular 3D pose estimation via flow matching}, 
      author={Ti Wang and Xiaohang Yu and Mackenzie Weygandt Mathis},
      year={2026},
      eprint={2602.05755},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2602.05755}, 
}

License

Model weights are for non-commerical use. Please contact the EPFL TTO for future information. We thank the Swiss National Science Foundation (SNSF Project # 320030-227871) and the Kavli Foundation for financial support.