QPGesture: Quantization-Based and Phase-Guided Motion Matching for Natural Speech-Driven Gesture Generation (2305.11094v1)

Abstract: Speech-driven gesture generation is highly challenging due to the random jitters of human motion. In addition, there is an inherent asynchronous relationship between human speech and gestures. To tackle these challenges, we introduce a novel quantization-based and phase-guided motion-matching framework. Specifically, we first present a gesture VQ-VAE module to learn a codebook to summarize meaningful gesture units. With each code representing a unique gesture, random jittering problems are alleviated effectively. We then use Levenshtein distance to align diverse gestures with different speech. Levenshtein distance based on audio quantization as a similarity metric of corresponding speech of gestures helps match more appropriate gestures with speech, and solves the alignment problem of speech and gestures well. Moreover, we introduce phase to guide the optimal gesture matching based on the semantics of context or rhythm of audio. Phase guides when text-based or speech-based gestures should be performed to make the generated gestures more natural. Extensive experiments show that our method outperforms recent approaches on speech-driven gesture generation. Our code, database, pre-trained models, and demos are available at https://github.com/YoungSeng/QPGesture.

• The paper introduces QPGesture, a novel framework for generating natural speech-driven gestures by addressing motion jitter and speech-gesture asynchronicity.
• The method uses a VQ-VAE to quantize gestures into discrete units and Levenshtein distance-based audio-gesture matching with phase guidance for synchronization.
• Experiments show QPGesture generates gestures that are objectively and subjectively more human-like and appropriate than state-of-the-art methods.

An Expert Overview of "QPGesture: Quantization-Based and Phase-Guided Motion Matching for Natural Speech-Driven Gesture Generation"

The paper "QPGesture: Quantization-Based and Phase-Guided Motion Matching for Natural Speech-Driven Gesture Generation" presents an innovative approach to the generation of speech-driven gestures, aiming to overcome challenges related to the inherent asynchronicity between speech and gestures, as well as random jittering in human motions. This subject area has garnered substantial research interest due to the complex interplay between verbal language and nonverbal gesticulation, which significantly enhances human communication. The authors propose a robust framework that integrates quantization techniques and phase guidance to improve the synthesis of gestures that accompany speech, thereby enhancing the naturalness and appropriateness of the generated motions.

Methodological Framework

The core of the proposed method relies on the quantization of gesture data through a vector quantized variational autoencoder (VQ-VAE), which introduces a codebook for representing discrete gesture units. This quantization effectively reduces the high-dimensional complexity of gesture data, alleviating random jittering that often plagues gesture synthesis. Each code signifies a unique gesture, which simplifies the motion matching process.

For the alignment challenge between speech and gestures, the authors utilize a Levenshtein distance-based approach, leveraging audio quantization to measure the similarity between speech and corresponding gestures. This provides a mechanism to address synchronization issues, allowing more accurate matching of gestures to speech sequences. Furthermore, the incorporation of phase guidance optimizes the selection of gestures based on semantic context and auditory rhythm, contributing to improved naturalness in gesture generation.

Experimental Results

Extensive experiments on the BEAT dataset demonstrate the efficacy of the proposed framework compared to state-of-the-art methods. Objective evaluations such as the Hellinger distance and Fréchet Gesture Distance (FGD) indicate that QPGesture yields gestures with velocity profiles and distribution metrics closely aligning with real human motion. Additionally, subjective assessments reveal that gestures generated by the framework surpass existing models in human-likeness and appropriateness, even reaching levels comparable to ground-truth data.

Implications and Future Directions

The implications of this research are multifaceted, impacting both theoretical advancements in gesture synthesis as well as practical applications in artificial intelligence and human-computer interaction domains. By quantizing gestures into discrete units and preserving contextual semantics through phase-guided matching, this framework can be instrumental in developing more sophisticated virtual agents and robots that communicate naturally with humans.

Future developments building on this work may explore the integration of additional modalities such as facial expressions and emotional cues to enhance speech-driven gesture generation further. Moreover, the design choices outlined in the paper provide a basis for future research to refine quantization methods and explore more refined synchrony metrics, potentially leading to breakthroughs in the seamless integration of non-verbal behaviors in multi-modal communication systems.

Conclusion

This paper contributes significantly to the field of speech-driven gesture generation, presenting a comprehensive framework that effectively addresses key challenges of motion jittering and asynchronicity. By leveraging advanced quantization techniques and phase guidance, the authors have paved the way for creating more natural, contextually appropriate gestures, advancing both theoretical understanding and practical capabilities in AI-driven communication technologies.