Dance serves as a powerful medium for expressing human emotions, but the lifelike generation of dance is still a considerable challenge. Recently, diffusion models have showcased remarkable generative abilities across various domains. They hold promise for human motion generation due to their adaptable many-to-many nature. Nonetheless, current diffusion-based motion generation models often create entire motion sequences directly and unidirectionally, lacking focus on the motion with local and bidirectional enhancement. When choreographing high-quality dance movements, people need to take into account not only the musical context but also the nearby music-aligned dance motions. To authentically capture human behavior, we propose a Bidirectional Autoregressive Diffusion Model (BADM) for music-to-dance generation, where a bidirectional encoder is built to enforce that the generated dance is harmonious in both the forward and backward directions. To make the generated dance motion smoother, a local information decoder is built for local motion enhancement. The proposed framework is able to generate new motions based on the input conditions and nearby motions, which foresees individual motion slices iteratively and consolidates all predictions. To further refine the synchronicity between the generated dance and the beat, the beat information is incorporated as an input to generate better music-aligned dance movements. Experimental results demonstrate that the proposed model achieves state-of-the-art performance compared to existing unidirectional approaches on the prominent benchmark for music-to-dance generation.

翻译：舞蹈是人类情感表达的强大媒介，但生成逼真的舞蹈动作仍是一项重大挑战。近年来，扩散模型在多个领域展现出卓越的生成能力，因其灵活的多对多特性，有望应用于人体运动生成。然而，当前基于扩散的运动生成模型通常直接且单向地生成完整运动序列，缺乏对局部及双向增强运动的关注。在编排高质量舞蹈动作时，人们不仅需要考虑音乐背景，还需兼顾邻近音乐对齐的舞蹈动作。为真实捕捉人类行为，我们提出了一种用于音乐到舞蹈生成的双向自回归扩散模型（BADM），其中构建了双向编码器，确保生成的舞蹈在正向和反向维度上均和谐一致。为使生成的舞蹈动作更加平滑，我们构建了局部信息解码器以增强局部运动。该框架能够根据输入条件和邻近动作生成新动作，迭代预测各个运动片段并整合所有预测结果。为进一步提升生成舞蹈与节拍的同步性，将节拍信息作为输入，以生成更贴合音乐节拍的舞蹈动作。实验结果表明，在音乐到舞蹈生成的主流基准测试中，所提模型相较于现有单向方法取得了最先进的性能。