CORE4D: A 4D Human-Object-Human Interaction Dataset for Collaborative Object REarrangement
<|reference_start|>CORE4D: A 4D Human-Object-Human Interaction Dataset for Collaborative Object REarrangement: 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.<|reference_end|>
@article{zhang2024core4d:,
    title={CORE4D: A 4D Human-Object-Human Interaction Dataset for Collaborative
  Object REarrangement},
    author={Chengwen Zhang, Yun Liu, Ruofan Xing, Bingda Tang, Li Yi},
    journal={arXiv preprint arXiv:2406.19353},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.19353},
    primaryClass={cs.CV}
}