Learning Hierarchical Cross-Modal Association for Co-Speech Gesture Generation (2203.13161v1)

Abstract: Generating speech-consistent body and gesture movements is a long-standing problem in virtual avatar creation. Previous studies often synthesize pose movement in a holistic manner, where poses of all joints are generated simultaneously. Such a straightforward pipeline fails to generate fine-grained co-speech gestures. One observation is that the hierarchical semantics in speech and the hierarchical structures of human gestures can be naturally described into multiple granularities and associated together. To fully utilize the rich connections between speech audio and human gestures, we propose a novel framework named Hierarchical Audio-to-Gesture (HA2G) for co-speech gesture generation. In HA2G, a Hierarchical Audio Learner extracts audio representations across semantic granularities. A Hierarchical Pose Inferer subsequently renders the entire human pose gradually in a hierarchical manner. To enhance the quality of synthesized gestures, we develop a contrastive learning strategy based on audio-text alignment for better audio representations. Extensive experiments and human evaluation demonstrate that the proposed method renders realistic co-speech gestures and outperforms previous methods in a clear margin. Project page: https://alvinliu0.github.io/projects/HA2G

• The paper introduces HA2G, a dual-module framework that hierarchically associates audio features with gesture synthesis to enhance avatar realism.
• It employs a Hierarchical Audio Learner and a Hierarchical Pose Inferer to capture nuanced motion details and maintain beat consistency.
• Experimental results show lower Fréchet Gesture Distance and higher gesture diversity, underscoring the model’s superior performance over conventional methods.

An Expert Overview of "Learning Hierarchical Cross-Modal Association for Co-Speech Gesture Generation"

This paper proposes a novel approach named Hierarchical Audio-to-Gesture (HA2G) to address the challenge of generating co-speech gestures for virtual avatars. The core contribution of the paper is the integration of a hierarchical framework that leverages cross-modal associations between speech and gestures, going beyond typical holistic generation methods that handle gesture synthesis in an undifferentiated manner. Herein, key components and results from the paper are critically analyzed.

Core Contributions and Methodology

The paper introduces a dual-module framework: the Hierarchical Audio Learner and the Hierarchical Pose Inferer, both designed to improve the granularity and fidelity of gesture synthesis:

1. Hierarchical Audio Learner: This module extracts audio features at multiple semantic levels using a hierarchy within a neural encoder architecture. Different levels of the audio are projected into corresponding body movements, acknowledging that distinct audio features (such as high-level semantics versus low-level beats) influence different gesture types. Contrastive learning is employed here to better integrate audio and text data, enhancing feature discriminativeness across modalities—enabling the model to learn more precise and contextually appropriate audio-gesture mappings.
2. Hierarchical Pose Inferer: By enforcing a tree-like generation approach, this module synthesizes gestures sequentially from coarse to fine details. The output is a hierarchical sequence of poses generated by leveraging a bidirectional GRU architecture, allowing for in-depth capture of the nuanced motion patterns inherent in different body parts, particularly the often-overlooked subtlety of finger movements.
3. Physical Constraints and Style Adaptation: An homage to the physical realism, the authors also incorporate physical constraints to ensure the plausibility of generated motion as well as adaptive style coordinators that tailor the generated gestures to specific speaker styles inferred from reference frames.

Experimental Result Highlights

Extensive experimental evaluations highlight the model's superior performance across several metrics:

• Fréchet Gesture Distance (FGD): The HA2G method yields lower FGD values than competing methods, indicating closer alignment with the distribution of real gesture data.
• Beat Consistency (BC) and Diversity: Higher scores in BC and diversity metrics confirm HA2G’s capability to sustain synchronous motion with audio and generate varied gesture outputs, respectively. This is particularly important as it demonstrates the model's ability to synthesize gestures that complement speech both temporally and semantically.

Implications and Future Work

The implications of this research span both theoretical foundations and practical applications. Theoretically, the work underscores the importance of recognizing the intrinsic hierarchy in both speech and gestural data, urging future models in this domain to embrace granular learning processes. Practically, such advancements fuel the development of more expressive and interactive digital avatars, enhancing human-computer interaction scenarios in areas like virtual customer service, education, and entertainment.

Future research may extend this framework to accommodate diverse languages and dialects, exploring the potential of HA2G in multilingual environments. There’s also scope for adapting such models for full-body motion synthesis or addressing the specifics of gesture styles influenced by cultural nuances, thereby broadening the spectrum of applicability.

In conclusion, the paper introduces a refined methodology that thoughtfully incorporates hierarchical associations across distinct modalities. By doing so, it significantly advances the landscape of co-speech gesture generation, warranting continued investigation and application in various AI-driven interactive systems.