Learning Quantised Structure-Preserving Motion Representations for Dance Fingerprinting

TL;DR

DANCEMATCH is a novel framework that creates compact, discrete motion signatures from raw video to enable efficient large-scale dance retrieval and analysis.

Contribution

It introduces a new motion quantisation method combined with transformers and a retrieval engine, facilitating scalable and interpretable dance fingerprinting.

Findings

Robust retrieval across diverse dance styles

Strong generalisation to unseen choreographies

Efficient sub-linear large-scale retrieval

Abstract

We present DANCEMATCH, an end-to-end framework for motion-based dance retrieval, the task of identifying semantically similar choreographies directly from raw video, defined as DANCE FINGERPRINTING. While existing motion analysis and retrieval methods can compare pose sequences, they rely on continuous embeddings that are difficult to index, interpret, or scale. In contrast, DANCEMATCH constructs compact, discrete motion signatures that capture the spatio-temporal structure of dance while enabling efficient large-scale retrieval. Our system integrates Skeleton Motion Quantisation (SMQ) with Spatio-Temporal Transformers (STT) to encode human poses, extracted via Apple CoMotion, into a structured motion vocabulary. We further design DANCE RETRIEVAL ENGINE (DRE), which performs sub-linear retrieval using a histogram-based index followed by re-ranking for refined matching. To facilitate reproducible research, we release DANCETYPESBENCHMARK, a pose-aligned dataset annotated with quantised motion tokens. Experiments demonstrate robust retrieval across diverse dance styles and strong generalisation to unseen choreographies, establishing a foundation for scalable motion fingerprinting and quantitative choreographic analysis.