Fan Yang*, Quanting Xie†, Atsunori Moteki*, Shoichi Masui*, Shan Jiang*, Kanji Uchino*, Yonatan Bisk†, and Graham Neubig†
* Fujitsu Research, † Carnegie Mellon University
Periodic human activities with implicit workflows are common in manufacturing, sports, and daily life. While short-term periodic activities---characterized by simple structures and high-contrast patterns---have been widely studied, long-term periodic workflows with low-contrast patterns remain largely underexplored. To bridge this gap, we introduce the first benchmark comprising 580 multimodal human activity sequences featuring long-term periodic workflows. The benchmark supports three evaluation tasks aligned with real-world applications: unsupervised periodic workflow detection, task completion tracking, and procedural anomaly detection. We also propose a lightweight, training-free baseline for modeling diverse periodic workflow patterns. Experiments show that: (i) our benchmark presents significant challenges to both unsupervised periodic detection methods and zero-shot approaches based on powerful large language models (LLMs); (ii) our baseline outperforms competing methods by a substantial margin in all evaluation tasks; and (iii) in real-world applications, our baseline demonstrates deployment advantages on par with traditional supervised workflow detection approaches, eliminating the need for annotation and retraining.
Periodic spatiotemporal activity in a yoga example. While existing studies focus on short-term periods with simple structures and high-contrast patterns, we investigate a novel direction on long-term periods that involve complex workflows with low-contrast patterns.
Left: Illustration of our benchmark. TopRight: Statistics of our dataset. BottomRight: Annotation of our benchmark. We collected 580 long-term workflows of periodic human activities, characterized by a compact dataset yet encompassing a wide variety of real-world periodic tasks, including factory production, exercise training, and shuttle routes.
@InProceedings{Fan_2026_WACV,
author = {Fan, Yang and Quanting, Xie and Atsunori, Moteki and Shoichi, Masui and Shan, Jiang and Yonatan, Bisk and Graham, Neubig},
title = {Unsupervised Discovery of Long-Term Spatiotemporal Periodic Workflows in Human Activities},
booktitle = {Proceedings of the Winter Conference on Applications of Computer Vision (WACV)},
month = {March},
year = {2026}
}