Abstract
3D hand pose estimation has garnered great attention in recent years due to its critical applications in human-computer interaction, virtual reality, and related fields. The accurate estimation of hand joints is essential for high-quality hand pose estimation. However, existing methods neglect the importance of Distal Phalanx Tip (TIP) and Wrist in predicting hand joints overall and often fail to account for the phenomenon of error accumulation for distal joints in gesture estimation, which can cause certain joints to incur larger errors, resulting in misalignments and artifacts in the pose estimation and degrading the overall reconstruction quality. To address this challenge, we propose a novel segmented architecture for enhanced hand pose estimation (EHPE). We perform local extraction of TIP and wrist, thus alleviating the effect of error accumulation on TIP prediction and further reduce the predictive errors for all joints on this basis. EHPE consists of two key stages: In the TIP and Wrist Joints Extraction stage (TW-stage), the positions of the TIP and wrist joints are estimated to provide an initial accurate joint configuration; In the Prior Guided Joints Estimation stage (PG-stage), a dual-branch interaction network is employed to refine the positions of the remaining joints. Extensive experiments on two widely used benchmarks demonstrate that EHPE achieves state-of-the-arts performance. Code is available at this https URL.
Abstract (translated)
近年来，3D手部姿态估计因其在人机交互、虚拟现实及相关领域的关键应用而备受关注。准确地估算手关节位置对于高质量的手部姿态估计至关重要。然而，现有方法往往忽略了远端指骨顶端（TIP）和手腕在预测整体手关节时的重要性，并且通常未能考虑到手势估计中远端关节误差累积的现象，这会导致某些关节出现更大的误差，从而导致姿态估计中的错位和伪影，降低整体重建质量。 为了解决这一挑战，我们提出了一种新型分段架构的增强型手部姿态估计算法（EHPE）。我们在局部提取TIP和手腕位置，以此减轻误差累积对TIP预测的影响，并在此基础上进一步减少所有关节的预测错误。EHPE由两个关键阶段组成：在第一阶段（TIP和腕关节抽取阶段，TW阶段），估算TIP和腕关节的位置以提供初步准确的手部关节配置；在第二阶段（先验引导关节估计阶段，PG阶段），采用双分支交互网络来优化剩余关节的位置。 在两项广泛使用的基准测试中进行的大量实验表明，EHPE达到了最先进的性能。代码可以在提供的链接处获取。
URL
https://arxiv.org/abs/2507.09560
