论文标题
Ray3D：基于射线的3D人姿势估计单眼绝对3D定位
Ray3D: ray-based 3D human pose estimation for monocular absolute 3D localization
论文作者
论文摘要
在本文中，我们提出了一种新型的单眼射线3D（RAY3D）绝对人类姿势，并使用校准相机进行估计。准确且可推广的绝对3D人类姿势估计单眼2D姿势输入是一个不适的问题。为了应对这一挑战，我们将输入从像素空间转换为3D归一化射线。这种转换使我们对摄像机内在参数更改的方法进行了强大的变化。为了处理野外摄像机外部参数变化，Ray3D明确地将相机外部参数作为输入，并共同对3D姿势射线和摄像机外部参数之间的分布进行建模。这种新颖的网络设计是Ray3D方法出色的概括性的关键。为了全面了解摄像机如何固有和外在参数变化影响绝对3D密钥点定位的准确性，我们对三个单人3D基准和一个合成基准进行了深入的系统实验。这些实验表明，我们的方法显着胜过现有的最新模型。我们的代码和合成数据集可在https://github.com/yxzhxn/ray3d上找到。
In this paper, we propose a novel monocular ray-based 3D (Ray3D) absolute human pose estimation with calibrated camera. Accurate and generalizable absolute 3D human pose estimation from monocular 2D pose input is an ill-posed problem. To address this challenge, we convert the input from pixel space to 3D normalized rays. This conversion makes our approach robust to camera intrinsic parameter changes. To deal with the in-the-wild camera extrinsic parameter variations, Ray3D explicitly takes the camera extrinsic parameters as an input and jointly models the distribution between the 3D pose rays and camera extrinsic parameters. This novel network design is the key to the outstanding generalizability of Ray3D approach. To have a comprehensive understanding of how the camera intrinsic and extrinsic parameter variations affect the accuracy of absolute 3D key-point localization, we conduct in-depth systematic experiments on three single person 3D benchmarks as well as one synthetic benchmark. These experiments demonstrate that our method significantly outperforms existing state-of-the-art models. Our code and the synthetic dataset are available at https://github.com/YxZhxn/Ray3D .
