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How to automatically synthesize natural-looking dance movements based on apiece of music is an incrementally popular yet challenging task. Most existingdata-driven approaches require hard-to-get paired training data and fail togenerate long sequences of motion due to error accumulation of autoregressivestructure. We present a novel 3D dance synthesis system that only needsunpaired data for training and could generate realistic long-term motions atthe same time. For the unpaired data training, we explore the disentanglementof beat and style, and propose a Transformer-based model free of reliance uponpaired data. For the synthesis of long-term motions, we devise a newlong-history attention strategy. It first queries the long-history embeddingthrough an attention computation and then explicitly fuses this embedding intothe generation pipeline via multimodal adaptation gate (MAG). Objective andsubjective evaluations show that our results are comparable to strong baselinemethods, despite not requiring paired training data, and are robust wheninferring long-term music. To our best knowledge, we are the first to achieveunpaired data training - an ability that enables to alleviate data limitationseffectively. Our code is released on https://github.com/BFeng14/RobustDancer

Robust Dancer: Long-term 3D Dance Synthesis Using Unpaired Data