What a Choreography Fingerprint Must Forget: Learning Motion Tokens Beyond Reconstruction

Motion tokenizers trained to reconstruct human movement are widely used as general-purpose motion representations. We study whether their tokens identify a choreography when a different person performs it, and introduce the task of Choreography Fingerprinting: given a short and possibly occluded fragment of a performance, a system must find other performances of the same choreography in an index that grows without retraining, and reject choreographies it does not contain. The task matters in practice: a viral choreography is repeated by thousands of people on social media, and courts have started to consider whether a sequence of movements was copied from another work. A fingerprint for this task has to ignore the performer, the camera and unseen parts while keeping the order of movements, and reconstruction requires neither. We show that codes can be invariant or have high entropy and still carry no information about the choreography. Following how dance notation records a choreography, as ordered movements of body parts with the floor path kept apart, we propose ChoreoMatch: trained on choreography labels without a reconstruction target, it infers hidden body parts without treating them as evidence and stores each third of a second in a few bytes. To test identification beyond the studio, we introduce the Choreography Fingerprinting Dataset of skeleton sequences extracted from amateur videos of viral choreographies filmed on phones, annotated by choreography. Our framework identifies choreographies in these videos and outperforms reconstruction tokenizers, suggesting that a choreography is recognized by what a representation is trained to keep rather than by what it can reconstruct.