Skeleton-only emotion recognition hits 37.23% on hidden test, near human 39%
Self-reported by the University of Tokyo's Naoto Nishida and Yoshio Ishiguro, an 11-model ensemble scored 37.23% Macro-F1 on the MMAC Challenge 2026 hidden test set, close to the human 39%.
ImportanceLocalEvidenceE2 unreplicated
Skeleton motion alone can now recognize 12 categories of performed emotion unseen by the performers: an 11-model ensemble by Naoto Nishida and Yoshio Ishiguro of the University of Tokyo scored 37.23% Macro-F1 on the MMAC Challenge 2026 hidden test set, close to the human 39% cited in the text, and the authors say it won the Best Performance Award.
The prior approach was 10-fold leave-performer-out cross-validation under the same protocol, yielding 36.80%, with a reproduced baseline of only 25.73%.
The 37.23% is author-reported, measured on the MMAC Challenge 2026 hidden test set; masking and counterfactual audits show the models rely on body-region motion evidence. The emotions are performed, and the results have not been independently reproduced; the data is the September 15 v2 preprint (arXiv:2609.02510), code at nawta/diema-challenge, author page nawta.github.io/mmac2026.