Abstract
Reconstructing a hand mesh from a single RGB image is a challenging task because hands are often occluded by objects. Most previous works attempted to introduce more additional information and adopt attention mechanisms to improve 3D reconstruction results, but it would increased computational complexity. This observation prompts us to propose a new and concise architecture while improving computational efficiency. In this work, we propose a simple and effective 3D hand mesh reconstruction network HandSSCA, which is the first to incorporate state space modeling into the field of hand pose estimation. In the network, we have designed a novel state space channel attention module that extends the effective sensory field, extracts hand features in the spatial dimension, and enhances hand regional features in the channel dimension. This design helps to reconstruct a complete and detailed hand mesh. Extensive experiments conducted on well-known datasets featuring challenging hand-object occlusions (such as FREIHAND, DEXYCB, and HO3D) demonstrate that our proposed HandSSCA achieves state-of-the-art performance while maintaining a minimal parameter count.
Abstract (translated)
从单个RGB图像中重构手网格是一个具有挑战性的任务,因为手经常被物体遮挡。之前的工作尝试引入更多附加信息并采用注意机制来提高3D重建结果,但会增加计算复杂度。这个观察结果促使我们提出一种新而简洁的架构,同时提高计算效率。在本文中,我们提出了一个简单而有效的3D手网格重构网络HandSSCA,这是第一个将状态空间建模应用到手姿态估计领域的网络。在网络中,我们设计了一个新颖的状态空间通道关注模块,扩展了有效的感官场,提取了手部的空间维度,并在通道维度上增强了手部区域特征。这种设计有助于重构完整和详细的手网格。在已知具有挑战性手-物体遮挡的数据集(如FREIHAND、DEXYCB和HO3D)上进行的广泛实验证明,我们的HandSSCA网络在保持最小参数计数的同时实现了最先进的性能。
URL
https://arxiv.org/abs/2405.01066
