MG-Former: A Transformer-Based Framework for Music-Driven 3D Conducting Gesture Generation
Ke Qiu, Yawen Qin, Tianzhi Jia, Xiaole Yang, Kaimin Wang, Kaixing Yang

TL;DR
This paper introduces TransConductor, a Transformer-based framework that generates realistic conducting gestures from music by leveraging a new dataset, advanced encoding, and a retrieval-based evaluation method.
Contribution
It presents a novel Transformer framework for music-driven conducting gesture synthesis, including a detailed dataset and a new evaluation protocol for artistic alignment.
Findings
TransConductor outperforms existing dance and conducting generation baselines.
The Transformer backbone and alignment loss improve gesture-music synchronization.
The dataset ConductorMotion enables detailed 3D conducting gesture analysis.
Abstract
Generating expressive conducting gestures from music is a challenging cross-modal motion synthesis problem: the output must follow long-range musical structure, preserve beat-level synchronization, and remain plausible as a fine-grained 3D human performance. Existing conducting-motion studies are often limited by sparse pose representations, small-scale data, or evaluation protocols that do not directly measure whether music and gesture are mutually aligned. This paper presents TransConductor, a Transformer-based framework for music-driven conducting gesture generation. We introduce ConductorMotion, a SMPL-parameter data construction pipeline that recovers detailed body motion from conducting videos and forms a dataset targeted at professional conducting gestures. Given acoustic descriptors extracted from audio and an initial pose, TransConductor uses a Trans-Temporal Music Encoder and…
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