Understanding how humans cooperatively rearrange household objects is critical for VR/AR and human-robot interaction. However, in-depth studies on modeling these behaviors are under-researched due to the lack of relevant datasets. We fill this gap by presenting CORE4D, a novel large-scale 4D human-object-human interaction dataset focusing on collaborative object rearrangement, which encompasses diverse compositions of various object geometries, collaboration modes, and 3D scenes. With 1K human-object-human motion sequences captured in the real world, we enrich CORE4D by contributing an iterative collaboration retargeting strategy to augment motions to a variety of novel objects. Leveraging this approach, CORE4D comprises a total of 11K collaboration sequences spanning 3K real and virtual object shapes. Benefiting from extensive motion patterns provided by CORE4D, we benchmark two tasks aiming at generating human-object interaction: human-object motion forecasting and interaction synthesis. Extensive experiments demonstrate the effectiveness of our collaboration retargeting strategy and indicate that CORE4D has posed new challenges to existing human-object interaction generation methodologies. Our dataset and code are available at https://github.com/leolyliu/CORE4D-Instructions.

翻译：理解人类如何协作重排家居物体对于VR/AR和人机交互研究至关重要。然而，由于缺乏相关数据集，针对此类行为的建模研究尚不充分。为填补这一空白，我们提出了CORE4D——一个专注于协作物体重排的大规模新型4D人-物-人交互数据集，其涵盖了多样化的物体几何组合、协作模式与三维场景。基于在真实世界采集的1K组人-物-人运动序列，我们通过提出迭代式协作重定向策略，将运动数据扩展适配至多种新物体，从而丰富了CORE4D的数据构成。依托该方法，CORE4D最终包含共11K组协作序列，覆盖3K种真实与虚拟物体形态。受益于CORE4D提供的丰富运动模式，我们针对人-物交互生成任务设立了两项基准测试：人-物运动预测与交互合成。大量实验证明了我们提出的协作重定向策略的有效性，并表明CORE4D为现有人-物交互生成方法提出了新的挑战。我们的数据集与代码已公开于https://github.com/leolyliu/CORE4D-Instructions。