大規模再構成モデルを用いた人と物体のインタラクション再構成
Reconstructing Humans and Objects in Interaction using Large Reconstruction Models
単一画像から人と物体の3Dインタラクションを再構成するフレームワークMILOを提案。大規模再構成モデル(LRM)の幾何学的特性を活用し、従来法より高精度な再構成を実現。
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著者: Agniv Chatterjee, Georgios Pavlakos
分類: cs.CV
原文アブストラクト
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.