生成逼真双人舞蹈交互动作，解决现有模型互动质量差问题
InterDance:Reactive 3D Dance Generation with Realistic Duet Interactions
- 构建大规模双人舞数据集，提升动作质量和多样性
- 提出新运动表征，精准描述双人互动细节
- 用扩散模型逐步优化互动真实感，适合舞蹈生成研究者
人类进行多种互动动作，其中双人舞蹈是最具挑战性的互动形式之一。然而，当前人体动作生成模型仍难以生成高质量的交互动作，尤其在双人舞蹈领域。一方面源于缺乏大规模高质量数据集；另一方面则因对交互动作表征不完整，且缺乏精细的交互优化机制。为此，我们提出InterDance，一个大规模双人舞蹈数据集，显著提升了动作质量、数据规模和舞种多样性。基于该数据集，我们设计了一种新型运动表征，能准确全面地描述交互动作。进一步提出基于扩散模型的框架，并引入交互精炼引导策略，逐步优化动作的真实感。大量实验验证了数据集与算法的有效性。
原文摘要 · Abstract (English)
Humans perform a variety of interactive motions, among which duet dance is one of the most challenging interactions. However, in terms of human motion generative models, existing works are still unable to generate high-quality interactive motions, especially in the field of duet dance. On the one hand, it is due to the lack of large-scale high-quality datasets. On the other hand, it arises from the incomplete representation of interactive motion and the lack of fine-grained optimization of interactions. To address these challenges, we propose, InterDance, a large-scale duet dance dataset that significantly enhances motion quality, data scale, and the variety of dance genres. Built upon this dataset, we propose a new motion representation that can accurately and comprehensively describe interactive motion. We further introduce a diffusion-based framework with an interaction refinement guidance strategy to optimize the realism of interactions progressively. Extensive experiments demonstrate the effectiveness of our dataset and algorithm.
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