InterDance:Reactive 3D Dance Generation with Realistic Duet Interactions

TL;DR

InterDance introduces a large-scale duet dance dataset and a diffusion-based generative framework with interaction refinement, enabling high-quality, realistic interactive duet dance motion synthesis.

Contribution

The paper presents a new large-scale duet dance dataset and a novel motion representation, along with a diffusion-based model for realistic interactive dance generation.

Findings

Enhanced motion quality and diversity in generated dances

Effective interaction refinement improves realism

Dataset and model outperform existing methods

Abstract

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.