Towards Unified Co-Speech Gesture Generation via Hierarchical Implicit Periodicity Learning
Abstract
Generating 3D-based body movements from speech shows great potential in extensive downstream applications, while it still suffers challenges in imitating realistic human movements. Predominant research efforts focus on end-to-end generation schemes to generate co-speech gestures, spanning GANs, VQ-VAE, and recent diffusion models. As an ill-posed problem, in this paper, we argue that these prevailing learning schemes fail to model crucial inter- and intra-correlations across different motion units, i.e. head, body, and hands, thus leading to unnatural movements and poor coordination. To delve into these intrinsic correlations, we propose a unified Hierarchical Implicit Periodicity (HIP) learning approach for audio-inspired 3D gesture generation. Different from predominant research, our approach models this multi-modal implicit relationship by two explicit technique insights: i) To disentangle the complicated gesture movements, we first explore the gesture motion phase manifolds with periodic autoencoders to imitate human natures from realistic distributions while incorporating non-period ones from current latent states for instance-level diversities. ii) To model the hierarchical relationship of face motions, body gestures, and hand movements, driving the animation with cascaded guidance during learning. We exhibit our proposed approach on 3D avatars and extensive experiments show our method outperforms the state-of-the-art co-speech gesture generation methods by both quantitative and qualitative evaluations. Code and models will be publicly available.
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 paper presents a comprehensive approach to co-speech gesture generation by introducing a novel framework that disentangles periodic and non-periodic movements, significantly advancing the field of gesture synthesis and its applications in various domains.
Methodology
The paper introduces a novel Hierarchical Implicit Periodicity (HIP) learning framework that effectively disentangles periodic and non-periodic gesture movements, addressing a significant gap in existing co-speech gesture generation methodologies. By employing periodic autoencoders and a hierarchical attribute guidance module, the authors successfully model the intricate inter- and intra-correlations among facial, body, and hand gestures. This approach is innovative as it combines insights from periodicity learning with a structured framework that enhances the naturalness and coherence of generated gestures.
Results & evaluation
The authors conduct extensive experiments on the BEAT and BEATv2 datasets, demonstrating that their method outperforms state-of-the-art approaches in both quantitative and qualitative metrics. The evaluation metrics used, including FGD, SRGR, and BeatAlign, provide a comprehensive assessment of the generated gestures' quality and alignment with audio. The results are well-presented, showcasing the effectiveness of the proposed method in generating realistic and contextually appropriate gestures.
Reproducibility
The paper mentions that code and models will be publicly available, which is a positive aspect for reproducibility. However, detailed implementation specifics, such as hyperparameters and training configurations, are provided, which aids in replicating the experiments. The use of consumer-level hardware for training also suggests accessibility for other researchers.
Limitations
The authors acknowledge limitations in their framework, particularly in the handling of periodic features and the implicit modeling of dependencies between gesture units. These aspects can lead to occasional failures in generating realistic motions, indicating areas for future improvement. Additionally, the model's performance may be sensitive to the choice of channel dimensionality in the disentanglement module.
Broader impact
The proposed method has significant implications for applications in virtual reality, human-computer interaction, and animation, where realistic gesture generation is crucial. By enhancing the realism of co-speech gestures, this research could improve communication in virtual environments, making interactions more engaging and lifelike. The paper presents a comprehensive approach to co-speech gesture generation by introducing a novel framework that disentangles periodic and non-periodic movements, significantly advancing the field of gesture synthesis and its applications in various domains.
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