Dance choreography for a piece of music is a challenging task, having to be creative in presenting distinctive stylistic dance elements while taking into account the musical theme and rhythm. It has been tackled by different approaches such as similarity retrieval, sequence-to-sequence modeling and generative adversarial networks, but their generated dance sequences are often short of motion realism, diversity and music consistency. In this paper, we propose a Music-to-Dance with Optimal Transport Network (MDOT-Net) for learning to generate 3D dance choreographies from music. We introduce an optimal transport distance for evaluating the authenticity of the generated dance distribution and a Gromov-Wasserstein distance to measure the correspondence between the dance distribution and the input music. This gives a well defined and non-divergent training objective that mitigates the limitation of standard GAN training which is frequently plagued with instability and divergent generator loss issues. Extensive experiments demonstrate that our MDOT-Net can synthesize realistic and diverse dances which achieve an organic unity with the input music, reflecting the shared intentionality and matching the rhythmic articulation. Sample results are found at https://www.youtube.com/watch?v=dErfBkrlUO8.

翻译：音乐片段的舞蹈舞蹈舞蹈舞蹈舞蹈舞蹈编程是一项具有挑战性的任务,必须具有创造性地展示独特的立体舞蹈元素,同时考虑到音乐主题和节奏。它通过类似性检索、顺序到顺序的建模和基因对抗网络等不同方法得到了解决,但是它们制作的舞蹈序列往往缺少运动现实、多样性和音乐一致性。在本文中,我们提议与最佳交通网络(MDOT-Net)一起学习如何从音乐中产生3D舞蹈舞蹈舞蹈舞蹈合体。我们引入了一种最佳的交通距离,以评估所制作的舞蹈分布的真实性,以及格罗莫夫-瓦瑟斯坦距离,以测量舞蹈分布和投入音乐之间的对应关系。这提供了一个定义明确且非差异化的培训目标,从而减轻了标准GAN培训的限制,而这种培训经常受到不稳定和不同发电机损失问题的困扰。广泛的实验表明,我们的MDOT-Net可以将现实和多样化的舞蹈与投入音乐结合起来,从而反映共同的故意性和匹配的节奏性交汇性。在 https://www.wayr=Os@www.yolv=Esublv.