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English(EN) EgoHand: Monocular 3D Hand Reconstruction from Egocentric Video

EgoHand模型在第一人称视角视频中实现最先进的3D手部重建

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了EgoHand，这是一种新颖的单目3D手部重建模型，专为第一人称视角视频设计。该模型通过利用包含1400万张图像的大型训练数据集，结合了合成数据、野外视频和运动捕捉数据，实现了最先进的性能。EgoHand采用了SOMA-Hand参数化表示和Transformer编码器，并通过相机线索和辅助2D分支进行增强，以提高几何一致性。评估结果显示其具有强大的泛化能力，并且用户偏好优于Apple EgoDex等现有模型，推理延迟约为9.69毫秒。 AI

影响 该模型可以通过在第一人称视角视频中实现更准确、响应更快的​​手部跟踪，从而改进增强现实和人机交互。

排序理由 这是一篇详细介绍新型3D手部重建模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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

EgoHand模型在第一人称视角视频中实现最先进的3D手部重建

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这是一篇详细介绍新型3D手部重建模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Chao-Yeh Chen, Tiffany Chen, Lin Duan, Tianyi Hu, Yu-Hsiang Huang, Umar Iqbal, Jiefeng Li, Edy Lim, Yanchen Liu, Yihan Pang, Hang Qiu, Jun Saito, Shao Su, Qingsi Wang, Xiufeng Xie ·

    EgoHand: Monocular 3D Hand Reconstruction from Egocentric Video

    arXiv:2610.30293v1 Announce Type: cross Abstract: We introduce EgoHand, a monocular 3D hand reconstruction model that achieves state-of-the-art performance on egocentric video. Central to our approach is the curation of a high-quality training corpus of 14 million images, combini…

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