论文标题
残留姿势：基于深度的3D人姿势估计的脱钩方法
Residual Pose: A Decoupled Approach for Depth-based 3D Human Pose Estimation
论文作者
论文摘要
我们建议利用卷积神经网络（CNN）可靠的2D姿势估计的最新进展，以估算多人人类手机相互作用（HRI）场景中深度图像中的3D姿势。我们的方法是基于这样的观察结果：从2D车身地标探测获得3D提升点的观察结果是对真实3D人类姿势的粗略估计，因此仅需要一个完善步骤。在这方面，我们的贡献是三倍。 （i）我们建议通过将2D姿势估计和3D姿势改进来从深度图像中进行3D姿势估计； （ii）我们提出了一种深入学习的方法，该方法会在3D姿势和真实的3D姿势之间回归残留姿势； （iii）我们表明，尽管它很简单，但我们的方法在两个公共数据集上取得了非常具有竞争力的结果，因此与最近的最新方法相比，多人HRI具有吸引力。
We propose to leverage recent advances in reliable 2D pose estimation with Convolutional Neural Networks (CNN) to estimate the 3D pose of people from depth images in multi-person Human-Robot Interaction (HRI) scenarios. Our method is based on the observation that using the depth information to obtain 3D lifted points from 2D body landmark detections provides a rough estimate of the true 3D human pose, thus requiring only a refinement step. In that line our contributions are threefold. (i) we propose to perform 3D pose estimation from depth images by decoupling 2D pose estimation and 3D pose refinement; (ii) we propose a deep-learning approach that regresses the residual pose between the lifted 3D pose and the true 3D pose; (iii) we show that despite its simplicity, our approach achieves very competitive results both in accuracy and speed on two public datasets and is therefore appealing for multi-person HRI compared to recent state-of-the-art methods.
