  Humans intuitively understand that inanimate objects do not move by
themselves, but that state changes are typically caused by human manipulation
(e.g., the opening of a book). This is not yet the case for machines. In part
this is because there exist no datasets with ground-truth 3D annotations for
the study of physically consistent and synchronised motion of hands and
articulated objects. To this end, we introduce ARCTIC -- a dataset of two hands
that dexterously manipulate objects, containing 2.1M video frames paired with
accurate 3D hand and object meshes and detailed, dynamic contact information.
It contains bi-manual articulation of objects such as scissors or laptops,
where hand poses and object states evolve jointly in time. We propose two novel
articulated hand-object interaction tasks: (1) Consistent motion
reconstruction: Given a monocular video, the goal is to reconstruct two hands
and articulated objects in 3D, so that their motions are spatio-temporally
consistent. (2) Interaction field estimation: Dense relative hand-object
distances must be estimated from images. We introduce two baselines ArcticNet
and InterField, respectively and evaluate them qualitatively and quantitatively
on ARCTIC. Our code and data are available at https://arctic.is.tue.mpg.de.
