Hangzhou Polytechnic

Automatic dance quality assessment has largely followed action recognition, judging which dance is performed rather than how well. RSDQ-Net maps target and reference performances onto a 19-node canonical skeleton, learns soft phase correspondence, models spatial execution deviation and temporal phase deviation separately, and constrains one latent quality variable with ordinal expertise labels and human pairwise preferences. Under a choreography-disjoint five-fold protocol on ImperialDance it reaches a QWK of 0.814 and ordinal MAE of 0.207, against 0.755 and 0.261 for the strongest baseline, 2T-GCN.

Automatic dance quality assessment has largely followed action recognition, judging which dance is performed rather than how well. RSDQ-Net maps target and reference performances onto a 19-node canonical skeleton, learns soft phase correspondence, models spatial execution deviation and temporal phase deviation separately, and constrains one latent quality variable with ordinal expertise labels and human pairwise preferences. Under a choreography-disjoint five-fold protocol on ImperialDance it reaches a QWK of 0.814 and ordinal MAE of 0.207, against 0.755 and 0.261 for the strongest baseline, 2T-GCN.