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 a Trans-Temporal Conducting Gesture Decoder to autoregressively predict SMPL pose parameters. To better assess artistic correspondence, we further build a retrieval-based evaluation model that embeds music and gestures into a shared space and yields FID, modality distance, multi-modality distance, and diversity metrics. Experiments show that TransConductor outperforms dance-generation and conducting-generation baselines, while ablations verify the benefits of the Transformer backbone and the proposed alignment loss.

翻译：从音乐中生成富有表现力的指挥手势是一项极具挑战性的跨模态运动合成问题：输出必须遵循长程音乐结构、保持节拍级同步，并且作为精细的三维人体表演保持合理性。现有的指挥运动研究通常受限于稀疏的姿态表示、小规模数据，或无法直接衡量音乐与手势是否相互对齐的评估协议。本文提出TransConductor，一种基于Transformer框架的音乐驱动指挥手势生成方法。我们引入ConductorMotion——一种SMPL参数数据构建流程，可从指挥视频中恢复详细的肢体运动，并构建一个针对专业指挥手势的数据集。给定从音频中提取的声学描述符和初始姿态，TransConductor利用跨时域音乐编码器与跨时域指挥手势解码器自回归地预测SMPL姿态参数。为更好地评估艺术对应关系，我们进一步构建基于检索的评估模型，将音乐与手势嵌入共享空间，并生成FID、模态距离、多模态距离及多样性等指标。实验表明，TransConductor优于舞蹈生成与指挥生成基线，消融实验验证了Transformer主干网络及所提出的对齐损失函数的有效性。