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Humans intuitively understand that inanimate objects do not move bythemselves, 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 partthis is because there exist no datasets with ground-truth 3D annotations forthe study of physically consistent and synchronised motion of hands andarticulated objects. To this end, we introduce ARCTIC -- a dataset of two handsthat dexterously manipulate objects, containing 2.1M video frames paired withaccurate 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 novelarticulated hand-object interaction tasks: (1) Consistent motionreconstruction: Given a monocular video, the goal is to reconstruct two handsand articulated objects in 3D, so that their motions are spatio-temporallyconsistent. (2) Interaction field estimation: Dense relative hand-objectdistances must be estimated from images. We introduce two baselines ArcticNetand InterField, respectively and evaluate them qualitatively and quantitativelyon ARCTIC. Our code and data are available at https://arctic.is.tue.mpg.de.

ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulation