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通过通用拓感知变压器模型的3D手姿势和网格估计
Shaoqi Yu1,2, Yintong Wang1,2, Lili Chen1,2
1Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China.
Frontiers in neurorobotics
|May 20, 2024
概括
这项研究介绍了HandGCNFormer,这是一个用于精确3D手姿势和人机交互中的网格估计的新型网络. 它有效地处理使用拓意识图形卷积网络的自我封闭和自我相似性挑战.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 机器学习 机器学习
背景情况:
- 精确的3D手姿势和网格估计对于人机交互 (HRI) 至关重要.
- 在手中的数据中,严重的自我封闭和高度的自我相似性对现有方法构成重大挑战.
- 现有的方法很难有效地解决由无形或类似的手关节引起的模两可.
研究的目的:
- 提出一个新的网络,HandGCNFormer,用于改进从深度图像的3D手姿势估计.
- 将手动动态拓学的先前知识纳入变压器网络.
- 为了提高远程上下文信息和局部拓连接的建模,以提高准确性.
主要方法:
- 开发了HandGCNFormer,这是一个利用深度图像输入的拓意识变压器网络.
- 引入了一个新的 Graphformer 解码器,附带了一个额外的节点偏移图形卷积层 (NoffGConv).
- 实现了拓意识的头部,取代了标准的MLP,以利用当地的拓约束.
主要成果:
- 在四个具有挑战性的数据集 (Hands2017,NYU,ICVL,MSRA) 中实现了3D手术中最先进的性能估计.
- 将框架扩展到HandGCNFormer-Mesh用于3D手网估计,产生Mano参数.
- 在HO-3D数据集上表现出具有竞争力的结果,特别是在处理遮方面.
结论:
- 拟议的HandGCNFormer通过整合拓先验和先进的网络架构,有效地解决了3D手姿势估计方面的挑战.
- 新的Graphformer解码器和拓感知头显著提高了准确性和稳定性.
- 扩展的HandGCNFormer-Mesh在复杂的,封闭的场景中显示出对3D手网估计的希望.
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