  Generating group dance motion from the music is a challenging task with
several industrial applications. Although several methods have been proposed to
tackle this problem, most of them prioritize optimizing the fidelity in dancing
movement, constrained by predetermined dancer counts in datasets. This
limitation impedes adaptability to real-world applications. Our study addresses
the scalability problem in group choreography while preserving naturalness and
synchronization. In particular, we propose a phase-based variational generative
model for group dance generation on learning a generative manifold. Our method
achieves high-fidelity group dance motion and enables the generation with an
unlimited number of dancers while consuming only a minimal and constant amount
of memory. The intensive experiments on two public datasets show that our
proposed method outperforms recent state-of-the-art approaches by a large
margin and is scalable to a great number of dancers beyond the training data.
