Full-Body Articulated Human-Object Interaction
Nan Jiang, Tengyu Liu, Zhexuan Cao, Jieming Cui, Zhiyuan zhang, Yixin, Chen, He Wang, Yixin Zhu, Siyuan Huang

TL;DR
This paper introduces CHAIRS, a large-scale dataset and a novel model for full-body articulated human-object interaction, enabling improved understanding and estimation of complex interactions involving movable joints.
Contribution
The paper presents the first dataset and model for full-body articulated human-object interaction, advancing fine-grained 3D interaction understanding.
Findings
CHAIRS dataset contains 16.2 hours of interactions with 46 participants and 81 objects.
The proposed model outperforms baselines in object pose estimation during interactions.
Learning geometrical relationships improves articulated object pose and shape reconstruction.
Abstract
Fine-grained capturing of 3D HOI boosts human activity understanding and facilitates downstream visual tasks, including action recognition, holistic scene reconstruction, and human motion synthesis. Despite its significance, existing works mostly assume that humans interact with rigid objects using only a few body parts, limiting their scope. In this paper, we address the challenging problem of f-AHOI, wherein the whole human bodies interact with articulated objects, whose parts are connected by movable joints. We present CHAIRS, a large-scale motion-captured f-AHOI dataset, consisting of 16.2 hours of versatile interactions between 46 participants and 81 articulated and rigid sittable objects. CHAIRS provides 3D meshes of both humans and articulated objects during the entire interactive process, as well as realistic and physically plausible full-body interactions. We show the value of…
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Code & Models
Videos
Full-Body Articulated Human-Object Interaction· youtube
Taxonomy
TopicsHuman Pose and Action Recognition · Hand Gesture Recognition Systems · Multimodal Machine Learning Applications
