论文标题
LG手：用本地和全球运动知识推进3D手姿势估计
LG-Hand: Advancing 3D Hand Pose Estimation with Locally and Globally Kinematic Knowledge
论文作者
论文摘要
从RGB图像中进行的3D手姿势估计受到获得深度信息的困难。因此，已经在估计2D手关节的3D手姿势上花费了大量关注。在本文中，我们利用了时空图卷积神经网络的优势，并提出了LG手，这是3D手姿势估计的强大方法。我们的方法将空间和时间依赖性同时纳入一个过程。我们认为运动学信息起着重要作用，这有助于3D手姿势估计的性能。因此，我们引入了两个新的目标功能，即角度和方向损失，以考虑手部结构。虽然角度损失涵盖了局部运动学信息，但方向损耗处理全球运动学。我们的LG手在第一人称手动作基准（FPHAB）数据集上取得了令人鼓舞的结果。我们还进行消融研究，以显示两个提出的目标函数的功效。
3D hand pose estimation from RGB images suffers from the difficulty of obtaining the depth information. Therefore, a great deal of attention has been spent on estimating 3D hand pose from 2D hand joints. In this paper, we leverage the advantage of spatial-temporal Graph Convolutional Neural Networks and propose LG-Hand, a powerful method for 3D hand pose estimation. Our method incorporates both spatial and temporal dependencies into a single process. We argue that kinematic information plays an important role, contributing to the performance of 3D hand pose estimation. We thereby introduce two new objective functions, Angle and Direction loss, to take the hand structure into account. While Angle loss covers locally kinematic information, Direction loss handles globally kinematic one. Our LG-Hand achieves promising results on the First-Person Hand Action Benchmark (FPHAB) dataset. We also perform an ablation study to show the efficacy of the two proposed objective functions.
