ARCTIC：一个用于灵巧双手手-物操控的数据集
ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulation
AI总结：
本文构建包含210万帧及精确3D网格、接触标注的双手灵巧操控数据集ARCTIC，并提出一致运动重建与交互场估计两项任务及ArcticNet、InterField基线。
摘要：
人类能直观地理解无生命物体不会自行移动，其状态变化通常由人类操控引起（例如翻开一本书）。但机器尚无法做到这一点。部分原因在于，目前缺乏可用于研究物理一致且同步的手与铰接物体运动的真值3D标注数据集。为此，我们提出了ARCTIC——一个双手灵巧操控物体的数据集，包含210万帧视频，并配有精确的3D手部和物体网格以及详细的动态接触信息。该数据集涵盖剪刀或笔记本电脑等物体的双手铰接操控，其中手部姿态与物体状态随时间共同演化。我们提出了两项新颖的铰接式手-物交互任务：（1）一致运动重建：给定单目视频，目标是以3D形式重建双手和铰接物体，使其运动在时空上保持一致。（2）交互场估计：必须从图像中估计密集的手-物相对距离。我们分别引入了两个基线模型ArcticNet和InterField，并在ARCTIC上对其进行了定性与定量评估。我们的代码和数据可在https://arctic.is.tue.mpg.de获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
