Assembly101 is a new procedural activity dataset featuring 4321 videos of
people assembling and disassembling 101 "take-apart" toy vehicles. Participants
work without fixed instructions, and the sequences feature rich and natural
variations in action ordering, mistakes, and corrections. Assembly101 is the
first multi-view action dataset, with simultaneous static (8) and egocentric
(4) recordings. Sequences are annotated with more than 100K coarse and 1M
fine-grained action segments, and 18M 3D hand poses. We benchmark on three
action understanding tasks: recognition, anticipation and temporal
segmentation. Additionally, we propose a novel task of detecting mistakes. The
unique recording format and rich set of annotations allow us to investigate
generalization to new toys, cross-view transfer, long-tailed distributions, and
pose vs. appearance. We envision that Assembly101 will serve as a new challenge
to investigate various activity understanding problems.

Assembly101 是一个新的过程性活动数据集，包含 4321 个视频，演示了人们组装和拆卸 101 个 “拆卸” 玩具车辆，并进行了注释以进行识别，预测和时间分割。 此外，他们还提出了一个新的错误检测任务。