MDD: A Dataset for Text-and-Music Conditioned Duet Dance Generation
We introduce Multimodal DuetDance (MDD), a diverse multimodal benchmark dataset designed for text-controlled and music-conditioned 3D duet dance motion generation. Our dataset comprises 620 minutes of high-quality motion capture data performed by professional dancers, synchronized with music, and detailed with over 10K fine-grained natural language descriptions. The annotations capture a rich movement vocabulary, detailing spatial relationships, body movements, and rhythm, making MDD the first dataset to seamlessly integrate human motions, music, and text for duet dance generation. We introduce two novel tasks supported by our dataset: (1) Text-to-Duet, where given music and a textual prompt, both the leader and follower dance motion are generated (2) Text-to-Dance Accompaniment, where given music, textual prompt, and the leader's motion, the follower's motion is generated in a cohesive, text-aligned manner. We include baseline evaluations on both tasks to support future research.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
DuetGen: Music Driven Two-Person Dance Generation via Hierarchical Masked Modeling
We present DuetGen, a novel framework for generating interactive two-person dances from music. The key challenge of this task lies in the inherent complexities of two-person dance interactions, where the partners need to…
Motion SynthesisDuolando: Follower GPT with Off-Policy Reinforcement Learning for Dance Accompaniment
We introduce a novel task within the field of 3D dance generation, termed dance accompaniment, which necessitates the generation of responsive movements from a dance partner, the "follower", synchronized with the lead da…
RhythmTeMuDance: Contrastive Alignment-Based Textual Control for Music-Driven Dance Generation
Existing music-driven dance generation approaches have achieved strong realism and effective audio-motion alignment. However, they generally lack semantic controllability, making it difficult to guide specific movements …
Cross-Modal RetrievalReactDance: Progressive-Granular Representation for Long-Term Coherent Reactive Dance Generation
Reactive dance generation (RDG) produces follower movements conditioned on guiding dancer and music while ensuring spatial coordination and temporal coherence. However, existing methods overemphasize global constraints a…
QuantizationTransflower: probabilistic autoregressive dance generation with multimodal attention
Dance requires skillful composition of complex movements that follow rhythmic, tonal and timbral features of music. Formally, generating dance conditioned on a piece of music can be expressed as a problem of modelling a …