论文标题
边缘：音乐的可编辑舞蹈一代
EDGE: Editable Dance Generation From Music
论文作者
论文摘要
舞蹈是一种重要的人类艺术形式，但是创造新的舞蹈可能很困难且耗时。在这项工作中，我们介绍了可编辑的舞蹈生成（Edge），这是一种用于编辑舞蹈一代的最先进方法，能够创建现实，身体上可行的舞蹈，同时仍然忠于输入音乐。 Edge使用基于变压器的扩散模型与Jukebox配对，强大的音乐功能提取器，并赋予了非常适合舞蹈的功能强大的编辑功能，包括缔合条件和内在舞会。我们引入了一种新的物理合理性指标，并通过（1）通过（1）通过（1）多个定量指标来评估我们的方法产生的舞蹈质量，以实现物理合理性，节奏对齐和多样性基准，以及更重要的是（2）大规模用户研究，表明对先前的正式方法有了显着改善。可以在我们的网站上找到来自我们模型的定性样本。
Dance is an important human art form, but creating new dances can be difficult and time-consuming. In this work, we introduce Editable Dance GEneration (EDGE), a state-of-the-art method for editable dance generation that is capable of creating realistic, physically-plausible dances while remaining faithful to the input music. EDGE uses a transformer-based diffusion model paired with Jukebox, a strong music feature extractor, and confers powerful editing capabilities well-suited to dance, including joint-wise conditioning, and in-betweening. We introduce a new metric for physical plausibility, and evaluate dance quality generated by our method extensively through (1) multiple quantitative metrics on physical plausibility, beat alignment, and diversity benchmarks, and more importantly, (2) a large-scale user study, demonstrating a significant improvement over previous state-of-the-art methods. Qualitative samples from our model can be found at our website.
