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English(EN) EMPIRE: Explicit Manipulation Planning as a Learnable Intermediate Representation for Egocentric Hand-Motion Forecasting

新的EMPIRE框架通过显式操作规划推进手部运动预测

研究人员推出了一种新颖的两阶段框架EMPIRE,旨在改进以自我为中心的手部运动预测。该方法首先从多模态上下文中学习显式操作规划,以理解手部与物体的交互,然后使用运动生成器基于这些固定的规划合成未来的手部运动。这种方法避免了操作学习与运动合成之间的干扰。EMPIRE还包含了EMPIRE-651K,这是一个拥有超过65万个训练窗口和111个任务的新数据集,并在手部运动预测方面展示了最先进的准确性。 AI

影响 该框架可以通过提高复杂任务中人类手部运动的预测能力,来增强智能交互系统的能力。

排序理由 该集群包含一篇详细介绍新框架和数据集的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新的EMPIRE框架通过显式操作规划推进手部运动预测

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该集群包含一篇详细介绍新框架和数据集的研究论文。[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) · Wen Wang, Ruibing Hou, Hong Chang, Shiguang Shan, Xilin Chen ·

    EMPIRE:显式操纵规划作为一种可学习的中间表示,用于以自我为中心的运动预测

    arXiv:2608.22449v1 Announce Type: cross Abstract: Forecasting dexterous hand motions from egocentric observations is fundamental to intelligent interactive systems. Existing VLM-based methods typically map observations directly to future motions, overlooking the underlying manipu…