MG-Former: A Transformer-Based Framework for Music-Driven 3D Conducting Gesture Generation
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 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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
This paper presents a significant advancement in the field of music-driven motion synthesis through the introduction of a Transformer-based framework for generating conducting gestures. The methodology effectively combines detailed pose representation with a novel evaluation approach, setting a new standard for future research in this area.
Methodology
The proposed methodology introduces a novel Transformer-based framework, TransConductor, which effectively addresses the challenge of generating conducting gestures from music. The use of SMPL parameters for detailed pose representation is a significant advancement over traditional sparse keypoint methods, allowing for a more nuanced and expressive depiction of conducting motions. The dual encoder-decoder architecture, comprising a Trans-Temporal Music Encoder and a Trans-Temporal Conducting Gesture Decoder, is well-conceived, leveraging the strengths of self-attention mechanisms to capture long-range dependencies in both music and gesture. The introduction of a retrieval-based evaluation model further enhances the methodology by providing a more meaningful assessment of the artistic correspondence between music and gestures, which is often overlooked in traditional metrics.
Results & evaluation
The experimental evaluation is robust, comparing the proposed model against established baselines in dance and conducting generation. The reported metrics (FID, M-Dist, MM-Dist, and diversity) indicate significant improvements in the quality and alignment of generated gestures with the corresponding music. The ablation studies convincingly demonstrate the contributions of the Transformer architecture and the alignment loss, supporting the claims of enhanced performance. The diversity in the dataset, covering various conducting styles and musical emotions, strengthens the validity of the results and showcases the model's adaptability.
Reproducibility
While the paper provides a detailed description of the methodology and experimental setup, it lacks specific implementation details such as code availability or dataset access, which are crucial for reproducibility. The absence of a demo or project URL further limits the ability of other researchers to validate and build upon this work.
Limitations
The paper acknowledges certain limitations, including the reliance on monocular reconstruction, which may not capture all nuances of conducting gestures, particularly baton motion and finger articulation. Additionally, the model struggles with very large gestures in energetic music and may lag during fast transitions. These limitations suggest areas for future research, such as incorporating hand-aware reconstruction techniques and exploring longer musical contexts.
Broader impact
The implications of this work extend beyond academic interest; it has potential applications in music education, virtual performances, and intelligent tutoring systems. By automating the generation of conducting gestures, this research could enhance interactive music learning environments and provide valuable tools for musicians and educators. The framework could also inspire further exploration of cross-modal motion synthesis in other artistic domains, promoting a deeper understanding of the interplay between music and movement. This paper presents a significant advancement in the field of music-driven motion synthesis through the introduction of a Transformer-based framework for generating conducting gestures. The methodology effectively combines detailed pose representation with a novel evaluation approach, setting a new standard for future research in this area.
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