3D human pose estimation has been a long-standing challenge in computer
vision and graphics, where multi-view methods have significantly progressed but
are limited by the tedious calibration processes. Existing multi-view methods
are restricted to fixed camera pose and therefore lack generalization ability.
This paper presents a novel Probabilistic Triangulation module that can be
embedded in a calibrated 3D human pose estimation method, generalizing it to
uncalibration scenes. The key idea is to use a probability distribution to
model the camera pose and iteratively update the distribution from 2D features
instead of using camera pose. Specifically, We maintain a camera pose
distribution and then iteratively update this distribution by computing the
posterior probability of the camera pose through Monte Carlo sampling. This
way, the gradients can be directly back-propagated from the 3D pose estimation
to the 2D heatmap, enabling end-to-end training. Extensive experiments on
Human3.6M and CMU Panoptic demonstrate that our method outperforms other
uncalibration methods and achieves comparable results with state-of-the-art
calibration methods. Thus, our method achieves a trade-off between estimation
accuracy and generalizability. Our code is in
this https URL

本文提出了一种使用概率三角测量模块的方法，将已标定的 3D 人体姿势估计方法推广到非标定场景，通过从 2D 特征迭代更新概率分布的方式实现相机姿势的后验概率，并将梯度直接反向传播到 2D 热图，从而实现端到端的训练。实验证明，本方法在估计精度和普适性之间取得了权衡，并在 Human3.6M 和 CMU Panoptic 数据集上的实验中优于其他非标定方法，并达到了与最先进标定方法相当的结果。