  Music and dance have always co-existed as pillars of human activities,
contributing immensely to the cultural, social, and entertainment functions in
virtually all societies. Notwithstanding the gradual systematization of music
and dance into two independent disciplines, their intimate connection is
undeniable and one art-form often appears incomplete without the other. Recent
research works have studied generative models for dance sequences conditioned
on music. The dual task of composing music for given dances, however, has been
largely overlooked. In this paper, we propose a novel extension, where we
jointly model both tasks in a dual learning approach. To leverage the duality
of the two modalities, we introduce an optimal transport objective to align
feature embeddings, as well as a cycle consistency loss to foster overall
consistency. Experimental results demonstrate that our dual learning framework
improves individual task performance, delivering generated music compositions
and dance choreographs that are realistic and faithful to the conditioned
inputs.
