HOLD: Category-agnostic 3D Reconstruction of Interacting Hands and
  Objects from Video
Zicong Fan Maria Parelli Maria Eleni Kadoglou Muhammed Kocabas Xu Chen Michael J. Black Otmar Hilliges
Since humans interact with diverse objects every day, the holistic 3D capture
of these interactions is important to understand and model human behaviour.
However, most existing methods for hand-object reconstruction from RGB either
assume pre-scanned object templates or heavily rely on limited 3D hand-object
data, restricting their ability to scale and generalize to more unconstrained
interaction settings. To this end, we introduce HOLD -- the first
category-agnostic method that reconstructs an articulated hand and object
jointly from a monocular interaction video. We develop a compositional
articulated implicit model that can reconstruct disentangled 3D hand and object
from 2D images. We also further incorporate hand-object constraints to improve
hand-object poses and consequently the reconstruction quality. Our method does
not rely on 3D hand-object annotations while outperforming fully-supervised
baselines in both in-the-lab and challenging in-the-wild settings. Moreover, we
qualitatively show its robustness in reconstructing from in-the-wild videos.
Code: https://github.com/zc-alexfan/hold