DMHead

Dual model head pose estimation. Fusion of SOTA models. 360° 6D HeadPose detection.

ToDo

https://github.com/choyingw/SynergyNet

  • MobileNetV2 backbone – not retrained

    image

    image　image

1. Summary

icon_design drawio (12)

2. Atmosphere

Kazam_screencast_00098_.mp4

3. Benchmark

  • Trained on 300W-LP (Custom, Mask-wearing face image augmentation)
  • Test on AFLW2000
    • June 20, 2022 
      Yaw: 3.6129, Pitch: 5.5801, Roll: 3.8468, MAE: 4.3466

4. Model Structure

  • INPUTS: Float32 [N,3,224,224]
  • OUTPUTS: Float32 [N,3], [Yaw,Roll,Pitch]

pinheadpose_1x3x224x224 onnx

5. Citation

@misc{https://doi.org/10.48550/arxiv.2005.10353,
    doi = {10.48550/ARXIV.2005.10353},
    url = {https://arxiv.org/abs/2005.10353},
    author = {Zhou, Yijun and Gregson, James},
    title = {WHENet: Real-time Fine-Grained Estimation for Wide Range Head Pose},
    publisher = {arXiv},
    year = {2020},
}


@misc{hempel20226d,
    title={6D Rotation Representation For Unconstrained Head Pose Estimation},
    author={Thorsten Hempel and Ahmed A. Abdelrahman and Ayoub Al-Hamadi},
    year={2022},
    eprint={2202.12555},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}


@INPROCEEDINGS{wu2021synergy,
  author={Wu, Cho-Ying and Xu, Qiangeng and Neumann, Ulrich},
  booktitle={2021 International Conference on 3D Vision (3DV)}, 
  title={Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry}, 
  year={2021}
}

GitHub

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