A Unit Enhancement and Guidance Framework for Audio-Driven Avatar Video Generation

Abstract: Audio-driven human animation technology is widely used in human-computer interaction, and the emergence of diffusion models has further advanced its development. Currently, most methods rely on multi-stage generation and intermediate representations, resulting in long inference time and issues with generation quality in specific foreground regions and audio-motion consistency. These shortcomings are primarily due to the lack of localized fine-grained supervised guidance. To address above challenges, we propose Parts-aware Audio-driven Human Animation, PAHA, a unit enhancement and guidance framework for audio-driven upper-body animation. We introduce two key methods: Parts-Aware Re-weighting (PAR) and Parts Consistency Enhancement (PCE). PAR dynamically adjusts regional training loss weights based on pose confidence scores, effectively improving visual quality. PCE constructs and trains diffusion-based regional audio-visual classifiers to improve the consistency of motion and co-speech audio. Afterwards, we design two novel inference guidance methods for the foregoing classifiers, Sequential Guidance (SG) and Differential Guidance (DG), to balance efficiency and quality respectively. Additionally, we build CNAS, the first public Chinese News Anchor Speech dataset, to advance research and validation in this field. Extensive experimental results and user studies demonstrate that PAHA significantly outperforms existing methods in audio-motion alignment and video-related evaluations. The codes and CNAS dataset will be released upon acceptance.

Overview of "PAHA: Parts-Aware Audio-Driven Human Animation with Diffusion Model"

The paper presented introduces PAHA, an innovative framework for generating audio-driven human animations. By leveraging a diffusion model, PAHA addresses challenges in localized generation quality and audio-motion consistency found in existing methods. The framework utilizes Parts-Aware Re-weighting (PAR) and Parts Consistency Enhancement (PCE) to enhance animation quality and ensure alignment with co-speech audio. Furthermore, the authors introduce the Chinese News Anchor Speech dataset (CNAS) to support research in this field.

Technical Contributions

The PAHA framework distinguishes itself through several key features:

1. Unified Video Diffusion Model (UniVDM): Unlike traditional approaches requiring multiple networks, PAHA employs a unified model to simultaneously handle reference images and noisy videos, reducing parametric overhead. This model is based on a 3D U-Net architecture that enables temporally consistent generation through carefully integrated audio layer enhancements.
2. Parts-Aware Re-weighting (PAR): The framework dynamically adjusts training loss weights based on pose confidence scores. By focusing on key areas (e.g., hands, face, and body), PAR enhances visual quality, particularly in regions critical for human animation.
3. Parts Consistency Enhancement (PCE): PAHA trains diffusion-based regional classifiers to subtly guide the generation process towards achieving better temporal consistency between motion and the audio spectrum during inference.
4. Guidance Methods for Inference: The authors propose Sequential Guidance (SG) for efficient generation and Differential Guidance (DG) for quality improvement, offering flexibility in achieving the desired balance between generation performance and computational efficiency.

Dataset Contributions

The authors introduce CNAS, the first publicly available Chinese News Anchor Speech dataset, which comprises detailed videos that support the evaluation of speech-driven human animation technologies. This dataset addresses the current scarcity of multilingual co-speech gesture datasets and is crafted specifically for tasks requiring nuanced gesture understanding.

Results and Implications

The empirical analysis indicates that PAHA outperforms existing methods such as SDT, ANGIE, MM-Diffusion, and S2G across various metrics, including Fréchet Gesture Distance (FGD), Beat Alignment Score (BAS), Synchronization-C (Sync-C), and Fréchet Video Distance (FVD). The framework provides enhanced realism and synchrony in generated videos, demonstrating its capability to address localized quality issues and improve audio-motion alignment comprehensively.

Theoretical and Practical Implications

The paper's contributions lie in both theoretical advancements and practical applications. Theoretically, it demonstrates how fine-grained control over diffusion model training can significantly impact generation quality. Practically, PAHA establishes a robust approach for developing high-fidelity human animations in applications ranging from video games and virtual reality to online education and digital assistants.

Future Directions

The research sets a precedent for further exploration into regional dynamics in human animation, inviting potential adaptations across different model architectures and application domains. The introduction of CNAS enriches the research landscape, although future expansions of this dataset could enhance its utility and breadth. Moreover, the focus on high-quality character animation may inspire developments in other areas such as emotion and expression synthesis or multilingual gesture databases, thereby broadening the scope and applicability of speech-driven animation technologies.

In conclusion, PAHA represents a significant advancement in audio-driven human animation frameworks, integrating parts-aware strategies with state-of-the-art diffusion models to achieve comprehensive improvements in video quality and alignment. By fostering both theoretical insights and practical tools, the paper contributes a valuable foundation for ongoing and future research in AI-driven content synthesis and multimedia applications.