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Dance Revolution: Long-Term Dance Generation with Music via Curriculum Learning
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Dance Revolution: Long-Term Dance Generation with Music via Curriculum Learning
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Dancing to music is one of human's innate abilities since ancient times. In machine learning research, however, synthesizing dance movements from music is a challenging problem. Recently, researchers synthesize human motion sequences through autoregressive models like recurrent neural network (RNN). Such an approach often generates short sequences due to an accumulation of prediction errors that are fed back into the neural network. This problem becomes even more severe in the long motion sequence generation. Besides, the consistency between dance and music in terms of style, rhythm and beat is yet to be taken into account during modeling. In this paper, we formalize the music-conditioned dance generation as a sequence-to-sequence learning problem and devise a novel seq2seq architecture to efficiently process long sequences of music features and capture the fine-grained correspondence between music and dance. Furthermore, we propose a novel curriculum learning strategy to alleviate error accumulation of autoregressive models in long motion sequence generation, which gently changes the training process from a fully guided teacher-forcing scheme using the previous ground-truth movements, towards a less guided autoregressive scheme mostly using the generated movements instead. Extensive experiments show that our approach significantly outperforms the existing state-of-the-arts on automatic metrics and human evaluation. We also make a demo video to demonstrate the superior performance of our proposed approach at https://www.youtube.com/watch?v=lmE20MEheZ8.
Forward citations
Cited by 6 Pith papers
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Text Dictates, Music Decorates: Energy-based Attention for Editable Dance Motion Generation
STREAM decouples text and music conditioning in a diffusion transformer via AdaLN for structure and BEAM for beats, plus new Motorica++ dataset and editability metrics, claiming SOTA music alignment with preserved semantics.
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DrawMotion: Generating 3D Human Motions by Freehand Drawing
DrawMotion is a diffusion-based framework that fuses text and hand-drawn stickman conditions via a Multi-Condition Module and training-free guidance to generate 3D human motions.
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TeMuDance: Contrastive Alignment-Based Textual Control for Music-Driven Dance Generation
TeMuDance enables text-based semantic control over music-conditioned dance generation by using motion as a bridge to align existing unpaired datasets and training a lightweight text branch on a frozen diffusion backbo...
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Text Dictates, Music Decorates: Energy-based Attention for Editable Dance Motion Generation
STREAM decouples text (via AdaLN) from music (via energy-based BEAM attention) to generate editable, musically aligned dance motions with a new annotated dataset and editability metric.
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GCDance: Genre-Controlled Music-Driven 3D Full Body Dance Generation
GCDance is a text-and-music-conditioned diffusion framework that generates genre-consistent 3D dance sequences and reports better results than prior methods on FineDance and AIST++.
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DanceDuo: Bridging Human Movement and AI Choreography
DanceDuo applies diffusion models for music-synchronized dance generation and pose estimation for user-AI performance comparison, with a user study reporting positive feedback on usability.
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