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Assembly101 is a new procedural activity dataset featuring 4321 videos ofpeople assembling and disassembling 101 "take-apart" toy vehicles. Participantswork without fixed instructions, and the sequences feature rich and naturalvariations in action ordering, mistakes, and corrections. Assembly101 is thefirst multi-view action dataset, with simultaneous static (8) and egocentric(4) recordings. Sequences are annotated with more than 100K coarse and 1Mfine-grained action segments, and 18M 3D hand poses. We benchmark on threeaction understanding tasks: recognition, anticipation and temporalsegmentation. Additionally, we propose a novel task of detecting mistakes. Theunique recording format and rich set of annotations allow us to investigategeneralization to new toys, cross-view transfer, long-tailed distributions, andpose vs. appearance. We envision that Assembly101 will serve as a new challengeto investigate various activity understanding problems.

Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activities