Reconstructing Humans and Objects in Interaction using Large Reconstruction Models

2026-08-27

AI Summary

本文提出MILO框架，利用大型重建模型从单张图像中恢复详细的3D人-物体交互，解决了因深度模糊、遮挡和物体形状变化导致的3D重建难题。

Abstract

Estimation of Human-Object Interactions in 3D (3D HOI) is a fundamental problem in 3D computer vision with applications in AR/VR, robotics, and embodied AI. However, reconstructing these interactions in 3D remains challenging due to depth ambiguities, occlusions, and object shape variability. Existing approaches are primarily concerned with reprojection and contact constraints, fitting parametric human models and object templates to 2D images. In this paper, we explore a different avenue. We present MILO, a framework that leverages the visual capabilities of Large Reconstruction Models (LRMs) to recover detailed 3D human-object interactions from a single image. Our key observation is that LRMs provide a powerful geometric scaffold that preserves relative human-object arrangement and proximity cues. This significantly simplifies the reconstruction procedure, reframing the problem as interpreting the LRM mesh: we segment it into human and object components, fit a parametric body model to the human part, and optionally align an object template to the object part (if such a template is available). MILO achieves strong reconstruction accuracy and outperforms existing baselines across multiple benchmarks and interaction scenarios. Our code is available at https://ac5113.github.io/MILO.

Problem

• 3D Human-Object Interactions
• Reconstruction
• Depth Ambiguities
• Occlusions
• Object Shape Variability

Innovation

• Large Reconstruction Models
• 3D Human-Object Interaction
• Geometric Scaffold