GraspHOI: Full-Body 3D Human-Object Reconstruction with Finger-Level Grasps from a Single In-the-Wild Image

2026-08-28

AI Summary

该研究解决了单图像中全身体三维人-物交互重建的问题，通过优化手指抓握和无类别物体重建的方法，提升了人体与物体的相对位置、手部准确性和接触合理性。

Abstract

Existing monocular full-body 3D human-object interaction (HOI) methods do not combine explicit finger-level grasp optimization with category-agnostic object reconstruction. Despite plausible body-object configurations, their fingers may float from or penetrate objects instead of forming a grasp. We present GraspHOI, the first framework that reconstructs a full-body 3D HOI from a single image while explicitly optimizing finger articulation against the reconstructed object. GraspHOI recovers object geometry directly, without predefined meshes or a fixed category vocabulary. It reconstructs the body, hands, and object separately, aligning them in metric camera space via depth-based registration and image-space alignment. Occlusion-aware palmar correspondences seat the object against the grasping hand, and contact-aware optimization refines arm and finger articulation to form surface contact without excessive penetration. Across four benchmarks and six baselines, GraspHOI improves relative human-object placement, hand accuracy, and contact plausibility. Full pipeline code will be released.

Problem

• 3D Human-Object Interaction
• Finger-Level Grasps
• Category-Agnostic Object Reconstruction

Innovation

• finger-level grasp optimization
• category-agnostic object reconstruction
• depth-based registration
• contact-aware optimization