With the rapid advancement of technologies such as virtual reality, augmented
reality, and gesture control, users expect interactions with computer
interfaces to be more natural and intuitive. Existing visual algorithms often
struggle to accomplish advanced human-computer interaction tasks, necessitating
accurate and reliable absolute spatial prediction methods. Moreover, dealing
with complex scenes and occlusions in monocular images poses entirely new
challenges. This study proposes a network model that performs parallel
processing of root-relative grids and root recovery tasks. The model enables
the recovery of 3D hand meshes in camera space from monocular RGB images. To
facilitate end-to-end training, we utilize an implicit learning approach for 2D
heatmaps, enhancing the compatibility of 2D cues across different subtasks.
Incorporate the Inception concept into spectral graph convolutional network to
explore relative mesh of root, and integrate it with the locally detailed and
globally attentive method designed for root recovery exploration. This approach
improves the model's predictive performance in complex environments and
self-occluded scenes. Through evaluation on the large-scale hand dataset
FreiHAND, we have demonstrated that our proposed model is comparable with
state-of-the-art models. This study contributes to the advancement of
techniques for accurate and reliable absolute spatial prediction in various
human-computer interaction applications.

利用虚拟现实、增强现实和手势控制等技术的快速发展，用户期望与计算机界面的交互更加自然和直观。本研究提出了一种网络模型，可从单目 RGB 图像中恢复相机空间中的 3D 手部网格，以实现准确和可靠的绝对空间预测。通过在大规模手部数据集 FreiHAND 上的评估，我们证明了我们提出的模型与最先进的模型具有可比性，为各种人机交互应用中的准确可靠的绝对空间预测技术的发展做出了贡献。