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English(EN) ScaleHP: Estimating Hand Pose in Metric Space

ScaleHP框架在度量空间中估计手部姿态

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了ScaleHP，一个在度量空间中估计手部姿态的新框架，解决了现有方法在相对坐标系中预测姿态的局限性。ScaleHP利用人手骨骼之间固有的比例关系，在不依赖外部深度模块的情况下推断出手部的绝对度量大小。该方法使用带有尺度令牌的基于Transformer的解码器来融合特征并求解度量坐标，在FreiHand、DexYCB和HO3Dv3等基准测试中取得了最先进的性能。 AI

影响 这项研究通过实现更精确的度量空间手部姿态估计，有望改善人机交互和机器人技术。

排序理由 该集群描述了一篇详细介绍特定计算机视觉任务新框架的最新研究论文。

在 arXiv cs.CV 阅读 →

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ScaleHP框架在度量空间中估计手部姿态

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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Ruitao Jing, Xingyu Chen, Hongyang Li, Qing Jiang, Yukai Shi, Lei Zhang ·

    ScaleHP：在度量空间中估计手部姿态

    arXiv:2606.25619v1 Announce Type: new Abstract: Accurate metric-space hand pose estimation (HPE) is essential for immersive human-computer interaction and robotics. However, most existing methods predict poses in a root-relative coordinate system and cannot estimate the hand in a…

  2. arXiv cs.CV TIER_1 English(EN) · Lei Zhang ·

    ScaleHP：在度量空间中估计手部姿态

    Accurate metric-space hand pose estimation (HPE) is essential for immersive human-computer interaction and robotics. However, most existing methods predict poses in a root-relative coordinate system and cannot estimate the hand in absolute metric scale. In this work, we observe t…

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