REVIEW 5 cited by
CORE4D: A 4D Human-Object-Human Interaction Dataset for Collaborative Object REarrangement
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
CORE4D: A 4D Human-Object-Human Interaction Dataset for Collaborative Object REarrangement
read the original abstract
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.
Forward citations
Cited by 5 Pith papers
-
CoMind: Understanding Collaborative Human Activity from Multiple Minds and Views
CoMind releases 41 h of synchronized multi-view cooking collaboration with social-cue annotations and three ToM-oriented benchmarks on which current VLMs score poorly until fine-tuned.
-
GIRAF: Towards Generalizable Human Interactions with Articulated Objects
A text-conditioned diffusion model using dynamic object-centric BPS, mixed-domain training, and contact augmentation produces generalizable full-body locomotion-to-articulated-object interaction sequences that beat ad...
-
MOCHI: Motion Enhancement of Collaborative Human-object Interactions
MOCHI enhances noisy collaborative human-object interaction captures via grasp optimization followed by diffusion-based full-body refinement that incorporates interaction information into single-person motion priors.
-
Learn Weightlessness: Imitate Non-Self-Stabilizing Motions on Humanoid Robot
The Weightlessness Mechanism lets humanoid robots imitate non-self-stabilizing motions by dynamically relaxing specific joints to exploit passive environmental contacts, generalizing from single demonstrations to vari...
-
Learn Weightlessness: Imitate Non-Self-Stabilizing Motions on Humanoid Robot
A weightlessness mechanism enables humanoid robots to dynamically relax joints for stable, contact-rich motions across diverse environments without task-specific tuning.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.