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Semantic Gesticulator: Semantics-Aware Co-Speech Gesture Synthesis (2405.09814v2)

Published 16 May 2024 in cs.GR, cs.CV, cs.SD, and eess.AS

Abstract: In this work, we present Semantic Gesticulator, a novel framework designed to synthesize realistic gestures accompanying speech with strong semantic correspondence. Semantically meaningful gestures are crucial for effective non-verbal communication, but such gestures often fall within the long tail of the distribution of natural human motion. The sparsity of these movements makes it challenging for deep learning-based systems, trained on moderately sized datasets, to capture the relationship between the movements and the corresponding speech semantics. To address this challenge, we develop a generative retrieval framework based on a LLM. This framework efficiently retrieves suitable semantic gesture candidates from a motion library in response to the input speech. To construct this motion library, we summarize a comprehensive list of commonly used semantic gestures based on findings in linguistics, and we collect a high-quality motion dataset encompassing both body and hand movements. We also design a novel GPT-based model with strong generalization capabilities to audio, capable of generating high-quality gestures that match the rhythm of speech. Furthermore, we propose a semantic alignment mechanism to efficiently align the retrieved semantic gestures with the GPT's output, ensuring the naturalness of the final animation. Our system demonstrates robustness in generating gestures that are rhythmically coherent and semantically explicit, as evidenced by a comprehensive collection of examples. User studies confirm the quality and human-likeness of our results, and show that our system outperforms state-of-the-art systems in terms of semantic appropriateness by a clear margin.

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Authors (7)
  1. Zeyi Zhang (4 papers)
  2. Tenglong Ao (9 papers)
  3. Yuyao Zhang (52 papers)
  4. Qingzhe Gao (7 papers)
  5. Chuan Lin (9 papers)
  6. Baoquan Chen (85 papers)
  7. Libin Liu (20 papers)
Citations (6)
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Summary

An Expert Overview of "Semantic Gesticulator: Semantics-Aware Co-Speech Gesture Synthesis"

Introduction

The paper "Semantic Gesticulator: Semantics-Aware Co-Speech Gesture Synthesis" introduces a novel framework to synthesize gestures accompanying speech, emphasizing their semantic correspondence. The framework addresses a critical challenge in co-speech gesture synthesis: generating semantically meaningful gestures, which often fall within the long tail of human motion distribution, thus making them difficult to model using traditional deep learning approaches.

Methodology

The authors present a comprehensive framework consisting of three principal components:

  1. Gesture Generative Model: Utilizes a GPT-2-based structure to predict future gesture tokens conditioned on past motion tokens and synchronized audio features.
  2. Generative Retrieval Framework: Leverages fine-tuned LLMs to retrieve suitable semantic gestures from a comprehensive motion library.
  3. Semantics-Aware Alignment Mechanism: Integrates retrieved semantic gestures with rhythmically generated motions, ensuring natural and semantically enriched gesture animation.

Gesture Tokenizer

A hierarchical Residual VQ-VAE (RVQ) model is employed to tokenize gesture sequences into discrete latent codes, enhancing the model's expressive capacity and enabling the handling of complex motions, including finger articulations. The motion representation is split into body and hand parts, each compressed individually to improve the quality and diversity of reconstructed gestures.

Gesture Generator

Built upon the GPT-2 architecture, the generator utilizes causal attention layers to predict a sequence of discrete gesture tokens. The model efficiently processes a variety of speech audio inputs, generating gestures that maintain rhythmic coherence with the speech input.

Generative Retrieval Framework

The retrieval framework is based on fine-tuning an LLM, which retrieves appropriate semantic gestures from a high-quality motion library according to speech transcripts. This framework not only enhances the semantic richness of the gestures but also determines their optimal timing within the speech context.

Dataset: Semantic Gesture Dataset (SeG)

The SeG Dataset is a key element of this research, consisting of over 200 types of semantic gestures encompassing body and hand movements. Each gesture in the dataset is recorded in multiple styles and variations using motion capture technology, providing a rich source of high-quality animation data for training and evaluation.

Experimental Results

Qualitative Evaluation

Visualization results demonstrate the system's capability to generate realistic and semantically meaningful gestures. The gestures align well with the speech content, enhancing communicative efficacy.

User Study

The system was evaluated via user studies against baselines such as GestureDiffuCLIP and CaMN. The studies focused on three criteria: human likeness, beat matching, and semantic accuracy. Results indicated that the proposed system outperforms the baselines, especially in terms of semantic accuracy, highlighting the effectiveness of the semantics-aware alignment mechanism.

Quantitative Metrics

The Fréchet Gesture Distance (FGD) and Semantic Score (SC) were employed to evaluate the motion quality and the semantic coherence between speech and gestures, respectively. The proposed system achieved lower FGD and higher SC compared to the baselines, confirming its superior performance in generating high-quality, semantically appropriate gestures.

Practical Implications and Future Directions

This research has significant implications for the development of virtual agents, avatars, and robots that can communicate naturally with humans through both speech and gestures. The comprehensive gesture dataset, robust retrieval framework, and innovative alignment mechanism pave the way for creating more expressive and effective communicative agents.

Future work could explore the extension of the gesture library to cover more diverse gestures and cultural contexts. Additionally, integrating more advanced LLMs and exploring multimodal learning techniques could further enhance the system's capability to generate contextually rich and culturally nuanced gestures.

Conclusion

The "Semantic Gesticulator" presents a significant advancement in co-speech gesture synthesis by focusing on the semantic richness of the generated gestures. Through a combination of generative models, advanced retrieval frameworks, and innovative alignment mechanisms, the system effectively bridges the gap between speech content and non-verbal communication, offering a robust solution for generating semantically and rhythmically coherent gestures. This work sets a foundation for future research in creating more natural and interactive virtual communicative agents.

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