CustomDancer: Customized Dance Recommendation by Text-Dance Retrieval
Yawen Qin, Ke Qiu, Qin Zhang

TL;DR
CustomDancer is a multimodal framework that improves personalized dance retrieval by aligning text, music, and motion data, supported by a new large-scale dataset.
Contribution
It introduces TD-Data, a comprehensive dance dataset, and proposes CustomDancer, a novel retrieval model that enhances text-based dance search accuracy.
Findings
CustomDancer achieves 10.23% Recall@1 on TD-Data.
The framework improves retrieval quality in benchmarks.
User studies favor CustomDancer over baselines.
Abstract
Dance serves as both a cultural cornerstone and a medium for personal expression, yet the rapid growth of online dance content has made personalized discovery increasingly difficult. Text-based dance retrieval offers a natural interface for users to search with choreographic intent, but it remains underexplored because dance requires simultaneous reasoning over linguistic semantics, musical rhythm, and full-body motion dynamics. We introduce TD-Data, a large-scale open dataset for text-dance retrieval, containing about 4,000 12-second dance clips, 14.6 hours of motion, 22 genres, and annotations from professional dance experts. On top of this dataset, we propose CustomDancer, a multimodal retrieval framework that aligns text with dance through a CLIP-based text encoder, music and motion encoders, and a music-motion blending module. CustomDancer achieves state-of-the-art performance on…
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