ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulation ...

• Main Authors: Fan, Zicong, Taheri, Omid, Tzionas, Dimitrios, Kocabas, Muhammed, Kaufmann, Manuel, Black, Michael J., Hilliges, Otmar
• Format: Report
• Language: unknown
• Published: arXiv 2022
• Subjects: Computer Vision and Pattern Recognition cs.CV; FOS Computer and information sciences; Arctic
• Online Access: https://dx.doi.org/10.48550/arxiv.2204.13662; https://arxiv.org/abs/2204.13662

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 ... : Project page: https://arctic.is.tue.mpg.de ...