TReViS: Temporal Repetition Structure Aware Video Synthesis for Self-supervised Repetitive Action Counting

arXiv:2609.24367 · cs.CV · Submitted 2026-09-21 · Read on arXiv

cs.CV

Submitted: 2026-09-21

Updated: 2026-09-21

Comments: Accepted for publication in Image and Vision Computing (Elsevier). This is the author-accepted manuscript and not the final published version of record. The DOI and link to the published version will be added when available

Code: https://github.com/yfqi/TReViS

License: http://creativecommons.org/licenses/by-nc-nd/4.0/

The gist: Fully supervised repetitive action counting (RAC) has achieved strong performance, but requires dense temporal annotations that are costly and difficult to scale.

Terminology

Abstract

Fully supervised repetitive action counting (RAC) has achieved strong performance, but requires dense temporal annotations that are costly and difficult to scale. We propose TReViS, a self-supervised video synthesis framework that enables training RAC models without any repetition labels. TReViS estimates the underlying temporal repetition structure of an unlabeled video via a Temporal Self-Similarity Matrix, infers its cycle statistics, and synthesizes new training sequences that preserve realistic repetition patterns while introducing controlled temporal variability. These synthesized videos are paired with pseudo-labels and used to train existing RAC architectures from scratch. Across multiple datasets and backbones, TReViS consistently outperforms prior self-supervised methods and achieves performance competitive with several supervised baselines, while remaining fully label-free, demonstrating the effectiveness of structure-aware video synthesis for label-free RAC. The source code is available at https://github.com/yfqi/TReViS.

Sources

Related papers