  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 diversity and the intricacy and
depth of dance movements. Moreover, the proposed POPDG model within the iDDPM
framework enhances dance diversity and, through the Space Augmentation
Algorithm, strengthens spatial physical connections between human body joints,
ensuring that increased diversity does not compromise generation quality. A
streamlined Alignment Module is also designed to improve the temporal alignment
between dance and music. 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.
