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CMA-OT: Hierarchical Expert Supervision for Dance-to-Music Generation

Paper recorded by Signals 4 on 2026-09-11 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-11 on arXiv · recorded by Signals 4 on 2026-09-14

Category: cs.AI · 人工智能 · first seen 2026-09-14

Abstract

Dance-to-music (D2M) generation aims to synthesize music that is rhythmically and stylistically aligned with dance videos. A key challenge arises from the semantic mismatch between sparse dance cues, such as rhythm and style, and the dense information required for music composition, including structure, instrumentation, and expressive dynamics. Existing methods typically rely on these sparse cues

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#83 most recent of 300 cs.AI papers we have recorded · ↑ newer: A Hybrid LSTM-XGBoost Framework for Multi-Horizon Stock Return Predict · ↓ older: ASTRIL-MPC: Autonomous Traversal Framework of Articulated Tracked Robo
Cite this page: CMA-OT: Hierarchical Expert Supervision for Dance-to-Music Generation: the #83 most recent of 300 cs.AI papers we have recorded (as of 2026-09-11). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/cma-ot-hierarchical-expert-supervision-for-dance-to-music-generation.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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