HandFoldingNet：一种基于多尺度特征引导折叠的三维手部姿态估计网络
HandFoldingNet: A 3D Hand Pose Estimation Network Using Multiscale-Feature Guided Folding of a 2D Hand Skeleton
摘要：
随着三维手势估计在各种人机交互应用中的应用越来越多，基于卷积神经网络（CNN）的估计模型已经被积极地开发。然而，现有的模型需要复杂的架构或冗余的计算资源，以可接受的精度进行交易。为了解决这一限制，本文提出了HandFoldingNet，这是一种准确有效的手部姿势估计器，它可以从归一化的3D手部点云输入中回归手部关节位置。该模型利用了一个基于折叠的解码器，将给定的2D手骨架折叠成相应的关节坐标。为了获得更高的估计精度，折叠由多尺度特征引导，多尺度特征包括全局和联合局部特征。实验结果表明，该模型优于现有的方法在三个手部姿势基准数据集与最低的模型参数要求。代码可在https://github.com/cwc1260/HandFold上获得。
英文摘要：
With increasing applications of 3D hand pose estimation in various human-computer interaction applications, convolution neural networks (CNNs) based estimation models have been actively explored. However, the existing models require complex architectures or redundant computational resources to trade with the acceptable accuracy. To tackle this limitation, this paper proposes HandFoldingNet, an accurate and efficient hand pose estimator that regresses the hand joint locations from the normalized 3D hand point cloud input. The proposed model utilizes a folding-based decoder that folds a given 2D hand skeleton into the corresponding joint coordinates. For higher estimation accuracy, folding is guided by multi-scale features, which include both global and joint-wise local features. Experimental results show that the proposed model outperforms the existing methods on three hand pose benchmark datasets with the lowest model parameter requirement. Code is available at https://github.com/cwc1260/HandFold.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
