Taming Diffusion Models for Audio-Driven Co-Speech Gesture Generation (2303.09119v2)

Abstract: Animating virtual avatars to make co-speech gestures facilitates various applications in human-machine interaction. The existing methods mainly rely on generative adversarial networks (GANs), which typically suffer from notorious mode collapse and unstable training, thus making it difficult to learn accurate audio-gesture joint distributions. In this work, we propose a novel diffusion-based framework, named Diffusion Co-Speech Gesture (DiffGesture), to effectively capture the cross-modal audio-to-gesture associations and preserve temporal coherence for high-fidelity audio-driven co-speech gesture generation. Specifically, we first establish the diffusion-conditional generation process on clips of skeleton sequences and audio to enable the whole framework. Then, a novel Diffusion Audio-Gesture Transformer is devised to better attend to the information from multiple modalities and model the long-term temporal dependency. Moreover, to eliminate temporal inconsistency, we propose an effective Diffusion Gesture Stabilizer with an annealed noise sampling strategy. Benefiting from the architectural advantages of diffusion models, we further incorporate implicit classifier-free guidance to trade off between diversity and gesture quality. Extensive experiments demonstrate that DiffGesture achieves state-of-theart performance, which renders coherent gestures with better mode coverage and stronger audio correlations. Code is available at https://github.com/Advocate99/DiffGesture.

• The paper introduces DiffGesture, a diffusion framework that generates synchronized co-speech gestures by modeling cross-modal audio and motion distributions.
• It employs a novel Diffusion Audio-Gesture Transformer to capture long-term temporal dependencies and mitigate GAN-related instability.
• Empirical results on benchmark datasets demonstrate lower Fréchet Gesture Distance and enhanced beat consistency, outperforming traditional GAN approaches.

Insights into Diffusion Models for Co-Speech Gesture Generation

The paper "Taming Diffusion Models for Audio-Driven Co-Speech Gesture Generation" presents an innovative approach to generating co-speech gestures using a diffusion model framework. The authors propose a novel methodology—DiffGesture, which leverages diffusion models to address the intrinsic challenges of synchronizing speech audio with corresponding human gestures. The paper asserts that existing methodologies, predominantly reliant on generative adversarial networks (GANs), encounter substantial limitations such as mode collapse and unstable training dynamics that impair the precision of audio-gesture joint distributions.

Technical Approach

The paper introduces the DiffGesture framework, structured around several core components offering distinctive contributions to improving the fidelity and coherence of gesture generation:

1. Diffusion Conditional Framework: The authors have formulated a diffusion-based process on clips of skeleton sequences and audio, aimed at capturing the nuanced cross-modal associations of speech and gesture. This presents a paradigm shift from traditional GANs by avoiding their common pitfalls, promising improved distribution coverage and training stability.
2. Diffusion Audio-Gesture Transformer: This novel architectural component addresses the challenge of modeling long-term temporal dependencies while attending to multimodal inputs (audio and initial gesture poses). It aligns the temporal dimension of the input data, enhancing the coherence of the generated gestures.
3. Stabilization and Guidance: The paper introduces a Diffusion Gesture Stabilizer to mitigate temporal inconsistencies typically introduced during the denoising process. Additionally, the use of implicit classifier-free guidance facilitates a balance between diversity and quality of generated gestures, key in capturing the inherent variability of human gestures.

Results

Empirical evaluation on prominent benchmarks, TED Gesture and TED Expressive datasets, showcases DiffGesture's superior performance in generating high-quality, synchronous gestures. Notably, the system achieves lower Fréchet Gesture Distance (FGD) values, indicating proximity to the distribution of real gestures. Furthermore, it exceeds baseline models in beat consistency and diversity metrics, illustrating its capability of producing varied and rhythmically synchronized gestures.

Implications and Future Directions

The implications of this work extend to various applications in human-machine interaction, particularly in animating virtual avatars for more natural human-computer interfaces. The diffusion-based framework paves the way for exploring more stable and flexible generative models in other temporal and conditional generation tasks.

Moving forward, potential areas of exploration include optimizing the computational efficiency of the diffusion processes and extending these models to more complex 3D gesture representations. Further integration with speech semantics could enhance the contextual relevance of gestures, providing a holistic solution to communication dynamics in virtual environments.

In summary, this paper contributes significantly to the field of co-speech gesture generation, proposing a robust alternative to GANs and setting a new benchmark for fidelity, coherence, and diversity in gesture synthesis.