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English(EN) HAT-4D: Lifting Monocular Video for 4D Multi-Object Interactions via Human-Agent Collaboration

HAT-4D框架从单视频中重建3D物体交互

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出HAT-4D，一个新颖的代理框架，旨在从单个单目视频中重建多个物体的3D几何、时间动态和物理交互。该方法集成了视觉语言模型（VLM）和人类在回路反馈机制，以克服多物体场景中的深度歧义和遮挡等挑战。HAT-4D旨在作为具身AI和训练VLA的可扩展数据引擎，并已用于创建MVOIK-4D，一个用于单目4D交互重建的新基准。 AI

影响 通过从单个视频中重建复杂的物体交互，从而能够更有效地为具身AI和VLA训练收集数据。

排序理由 该集群描述了一篇详细介绍用于视频4D重建的新颖框架和基准的研究论文。

在 arXiv cs.AI 阅读 →

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HAT-4D框架从单视频中重建3D物体交互

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该集群描述了一篇详细介绍用于视频4D重建的新颖框架和基准的研究论文。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Jiaxin Li, Yuxiang Wu, Zhenkai Zhang, Xinrui Shi, Haoyuan Wang, Yichen Zhao, Su Linxiang, Chenyang Yu, Mingyu Zhang, Yifan Ding, Boran Wen, Li Zhang, Ruiyang Liu, Yong-Lu Li ·

    HAT-4D：通过人机协作提升单目视频中的4D多目标交互

    arXiv:2606.28215v1 Announce Type: cross Abstract: Extracting dynamic 4D object interactions from massive, in-the-wild monocular videos offers a highly efficient data collection pathway for scaling Embodied AI and training VLAs. However, existing monocular 4D reconstruction method…

  2. arXiv cs.AI TIER_1 English(EN) · Yong-Lu Li ·

    HAT-4D：通过人机协作提升单目视频的4D多目标交互能力

    Extracting dynamic 4D object interactions from massive, in-the-wild monocular videos offers a highly efficient data collection pathway for scaling Embodied AI and training VLAs. However, existing monocular 4D reconstruction methods primarily focus on isolated objects, often faili…

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