EDMG: Towards Efficient Long Dance Motion Generation with Fundamental Movements from Dance Genres

Dance is an important art form in human culture, but creating new dances can be both challenging and time-consuming. In this paper, we propose a novel dance choreography framework, EDMG, designed to efficiently generate creative and long-lasting dance sequences conditioning on music and dance descriptions. In the first stage, we propose a flexible dance diffusion method, combined with dance genre description and descriptions of fundamental movements to generate the dance sequences. To achieve high computational efficiency and inference speed, EDMG designs a lightweight denoising module by using selective parallel scanning algorithm from Mamba2. This Parallel Mamba Denoiser reduces significantly the number of parameters and accelerates remarkably both the learning and inference processes. In the second stage, by designing a smoothing module with a long receptive field, we mitigate joint error accumulation that causes jittering movements and foot sliding, thereby enhancing the fluency and visual appeal of the dance movements. Furthermore, we extend the AIST++ dataset by adding detailed descriptions of dance genres and fundamental movements, using the Large Language Model (LLM). These descriptions further improve the choreography generation. EDMG is validated through extensive experiments, demonstrating that our method can both effectively and efficiently generate long-term dances suitable for various dance genres. Project URL: https://github.com/neymar277/EDMG.