SmoothSync: Dual-Stream Diffusion Transformers for Jitter-Robust Beat-Synchronized Gesture Generation from Quantized Audio
Abstract
Co-speech gesture generation is a critical area of research aimed at synthesizing speech-synchronized human-like gestures. Existing methods often suffer from issues such as rhythmic inconsistency, motion jitter, foot sliding and limited multi-sampling diversity. In this paper, we present SmoothSync, a novel framework that leverages quantized audio tokens in a novel dual-stream Diffusion Transformer (DiT) architecture to synthesis holistic gestures and enhance sampling variation. Specifically, we (1) fuse audio-motion features via complementary transformer streams to achieve superior synchronization, (2) introduce a jitter-suppression loss to improve temporal smoothness, (3) implement probabilistic audio quantization to generate distinct gesture sequences from identical inputs. To reliably evaluate beat synchronization under jitter, we introduce Smooth-BC, a robust variant of the beat consistency metric less sensitive to motion noise. Comprehensive experiments on the BEAT2 and SHOW datasets demonstrate SmoothSync's superiority, outperforming state-of-the-art methods by -30.6% FGD, 10.3% Smooth-BC, and 8.4% Diversity on BEAT2, while reducing jitter and foot sliding by -62.9% and -17.1% respectively. The code will be released to facilitate future research.
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
The main contribution of this paper is the introduction of the SmoothSync framework, which utilizes a dual-stream diffusion transformer architecture to generate high-quality, beat-synchronized co-speech gestures while addressing critical limitations in existing methods. The comprehensive evaluation of the proposed methodology demonstrates its effectiveness and potential impact on the field of gesture generation and human-computer interaction.
Methodology
The proposed SmoothSync framework introduces a dual-stream diffusion transformer architecture that effectively synchronizes audio and motion features, addressing key limitations in existing gesture generation methods. The methodology is innovative, incorporating a jitter-suppression loss and probabilistic audio quantization to enhance gesture diversity and temporal smoothness. The dual-stream architecture allows for modality-specific processing, which is a significant advancement over previous methods that either concatenate or process modalities independently. The introduction of the Smooth-BC metric for evaluating rhythmic alignment further strengthens the methodology by providing a more reliable assessment of synchronization under jitter conditions.
Results & evaluation
The experiments conducted on the BEAT2 and SHOW datasets demonstrate the effectiveness of SmoothSync, achieving state-of-the-art results across multiple metrics, including Fréchet Gesture Distance (FGD), Smooth-BC, and diversity measures. The quantitative results indicate substantial improvements in motion quality and rhythmic alignment, with detailed comparisons to existing methods. The paper provides comprehensive evaluations, including both qualitative and quantitative analyses, showcasing the model's robustness and generalization capabilities across different datasets.
Reproducibility
The paper mentions that the code will be released to facilitate future research, which is a positive aspect for reproducibility. However, specific implementation details, such as hyperparameters and training configurations, are provided, allowing for a clearer understanding of the model's training process. The use of standard datasets and metrics also supports reproducibility, although the absence of a direct URL for the code repository limits immediate access.
Limitations
While the paper presents significant advancements, it does not thoroughly address potential limitations, such as the scalability of the model to larger datasets or real-time applications beyond the evaluated scenarios. Additionally, the reliance on specific datasets may limit the generalizability of the findings. The paper could benefit from a more explicit discussion of the computational requirements and potential trade-offs in model complexity versus performance.
Broader impact
The SmoothSync framework has the potential to significantly enhance applications in virtual avatars, embodied AI systems, and other areas requiring synchronized gesture generation. By improving the quality and diversity of generated gestures, this research could contribute to more natural human-computer interactions and improve user experience in various applications, including gaming, virtual reality, and telecommunication. The main contribution of this paper is the introduction of the SmoothSync framework, which utilizes a dual-stream diffusion transformer architecture to generate high-quality, beat-synchronized co-speech gestures while addressing critical limitations in existing methods. The comprehensive evaluation of the proposed methodology demonstrates its effectiveness and potential impact on the field of gesture generation and human-computer interaction.
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