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English(EN) DigitCode: Symbolic Tokenization of Hand Motion by Anatomical Units

新的DigitCode系统为肢体动作提供符号化表示

研究人员开发了DigitCode,一种新的肢体动作符号化表示方法,将连续数据分解为离散的解剖单元。该方法比以前的方法提高了四分之三的准确性,并允许对肢体姿势进行符号化操作,例如编辑或修复生成的肢体。该系统还便于将肢体动作重新定向到机器人应用中,并包含一个名为HandTok的测试平台,用于比较不同的肢体分词器。 AI

影响 为AI提供更结构化和可编辑的人类动作表示,有望改善机器人和动画。

排序理由 该集群描述了一篇介绍肢体动作新符号化表示的新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的DigitCode系统为肢体动作提供符号化表示

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该集群描述了一篇介绍肢体动作新符号化表示的新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Haoyu Gu, Haotian Lu, Jingrun Du, Xiao-Ping Zhang ·

    DigitCode:通过解剖单元对运动手势进行符号化分词

    arXiv:2608.03127v1 Announce Type: cross Abstract: Hand motion carries the finest-grained information in human activity, yet the representations behind hand generation, understanding, and robot learning are overwhelmingly continuous--joint angles or MANO parameters. These are accu…