  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

Computer Vision and Pattern Recognition