This is the official PyTorch implementation of the approach described in the following paper:

Camera Distortion-aware 3D Human Pose Estimation in Video with Optimization-based Meta-Learning
Hanbyel Cho, Yooshin Cho, Jaemyung Yu, and Junmo Kim
IEEE/CVF International Conference on Computer Vision (ICCV), 2021

Existing 3D human pose estimation algorithms trained on distortion-free datasets suffer performance drop when applied to new scenarios with a specific camera distortion. In this paper, we propose a simple yet effective model for 3D human pose estimation in video that can quickly adapt to any distortion environment by utilizing MAML, a representative optimization-based meta-learning algorithm. We consider a sequence of 2D keypoints in a particular distortion as a single task of MAML. However, due to the absence of a large-scale dataset in a distorted environment, we propose an efficient method to generate synthetic distorted data from undistorted 2D keypoints. For the evaluation, we assume two practical testing situations depending on whether a motion capture sensor is available or not. In particular, we propose Inference Stage Optimization using bone-length symmetry and consistency. Extensive evaluation shows that our proposed method successfully adapts to various degrees of distortion in the testing phase and outperforms the existing state-of-the-art approaches. The proposed method is useful in practice because it does not require camera calibration and additional computations in a testing set-up.

Make sure you have the following dependencies installed before proceeding:

• Python 3+ distribution
• PyTorch >= 0.4.0

This work is licensed under CC BY-NC. See LICENSE for details. Third-party datasets are subject to their respective licenses. If you use our code/models in your research, please cite our paper:

@InProceedings{Cho_2021_ICCV,
    author    = {Cho, Hanbyel and Cho, Yooshin and Yu, Jaemyung and Kim, Junmo},
    title     = {Camera Distortion-Aware 3D Human Pose Estimation in Video With Optimization-Based Meta-Learning},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {11169-11178}
}

Part of our code is borrowed from VideoPose3D.
Please refer to their project page for further information.