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
this https URL

通过 CORE4D 数据集，我们补充了协作对象重新排列领域的研究空白，这个数据集关注于多种对象几何结构，协作模式和 3D 场景的组合，我们使用迭代协作重新定位策略来扩充模型，共有 11K 协作序列，涵盖 3K 真实和虚拟对象形状。通过 CORE4D 提供的丰富动作模式，我们分别进行人 - 物运动预测和交互合成两个任务的实验，证明了我们的策略有效性并给现有的人 - 物交互生成方法提出了新的挑战。