Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activities
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Abstract (arXiv)
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.
Key facts. Cited by 284 papers in the map (data through 7 Oct 2026). Builds on 42 of its 69 references that are in the map. First posted on arXiv 28 Mar 2022.
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42 of 69 references.
References · 42
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Citing papers per year: 2022: 7; 2023: 42; 2024: 66; 2025: 86; 2026: 83.
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