POPDG: Popular 3D Dance Generation with PopDanceSet

Generating dances that are both lifelike and well-aligned with music continues to be a challenging task in the cross- modal domain. This paper introduces PopDanceSet, the first dataset tailored to the preferences of young audiences, enabling the generation of aesthetically oriented dances. And it surpasses the AIST++ dataset in music genre di- versity and the intricacy and depth of dance movements. Moreover, the proposed POPDG model within the iD- DPM framework enhances dance diversity and, through the Space Augmentation Algorithm, strengthens spatial physi- cal connections between human body joints, ensuring that increased diversity does not compromise generation qual- ity. A streamlined Alignment Module is also designed to improve the temporal alignment between dance and mu- sic. Extensive experiments show that POPDG achieves SOTA results on two datasets. Furthermore, the paper also expands on current evaluation metrics. The dataset and code are available at https://github.com/Luke-Luo1/POPDG.