  We present an approach for estimating the period with which an action is
repeated in a video. The crux of the approach lies in constraining the period
prediction module to use temporal self-similarity as an intermediate
representation bottleneck that allows generalization to unseen repetitions in
videos in the wild. We train this model, called Repnet, with a synthetic
dataset that is generated from a large unlabeled video collection by sampling
short clips of varying lengths and repeating them with different periods and
counts. This combination of synthetic data and a powerful yet constrained
model, allows us to predict periods in a class-agnostic fashion. Our model
substantially exceeds the state of the art performance on existing periodicity
(PERTUBE) and repetition counting (QUVA) benchmarks. We also collect a new
challenging dataset called Countix (~90 times larger than existing datasets)
which captures the challenges of repetition counting in real-world videos.
Project webpage: https://sites.google.com/view/repnet .
