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Dance2Music: Automatic Dance-driven Music Generation

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arxiv 2107.06252 v2 pith:JEJ5MUBT submitted 2021-07-13 cs.SD cs.MMeess.AS

Gunjan Aggarwal , Devi Parikh This is my paper · ORCID
classification cs.SDcs.MMeess.AS
keywords musicdanceapproachvideodance2musicdemogeneratinggiven
verification ladder T0 review T1 audit T2 compute T3 formal
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Dance and music typically go hand in hand. The complexities in dance, music, and their synchronisation make them fascinating to study from a computational creativity perspective. While several works have looked at generating dance for a given music, automatically generating music for a given dance remains under-explored. This capability could have several creative expression and entertainment applications. We present some early explorations in this direction. We present a search-based offline approach that generates music after processing the entire dance video and an online approach that uses a deep neural network to generate music on-the-fly as the video proceeds. We compare these approaches to a strong heuristic baseline via human studies and present our findings. We have integrated our online approach in a live demo! A video of the demo can be found here: https://sites.google.com/view/dance2music/live-demo.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MoRAE: Flow-Friendly Self-Supervised Latents for Text-to-Motion Generation

    cs.CV 2026-07 conditional novelty 7.0 of 10

    Distilling frozen Motion-JEPA features into a compact 32-D latent whose geometry is coupled to the decoder lets a standard non-autoregressive flow-matching DiT reach state-of-the-art text-to-motion quality on HumanML3...

  2. Rethinking Diffusion for Text-Driven Human Motion Generation: Redundant Representations, Evaluation, and Masked Autoregression

    cs.CV 2024-11 conditional novelty 7.0 of 10

    A masked-autoregressive diffusion model trained on a compact essential-feature latent space claims state-of-the-art text-to-motion generation under a new essential-dimension evaluation protocol.

  3. Dance to Music Generation leveraging Pre-training with Unpaired data and Contrastive Alignment

    cs.SD 2026-07 conditional novelty 6.0 of 10

    Beat-guided contrastive alignment of pretrained MotionBERT/MERT features plus ControlNet conditioning of AudioLDM improves dance–music alignment on AIST++ over a MusicGen textual-inversion baseline while remaining com...

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