Abstract-Level Summary

This study introduces a novel method for generating expressive talking head videos, focusing on synthesizing realistic facial expressions and hand gestures driven by audio inputs. Recognizing the weak correlation between audio features and full-body gestures, the study redefines the problem into a two-stage process: first, generating hand poses directly from audio; second, using a diffusion model to create video frames incorporating these poses. Experimental results indicate that this approach surpasses current state-of-the-art methods like CyberHost and Vlogger in visual quality and synchronization accuracy, providing a new framework for audio-driven gesture generation.

Introduction Highlights

The research addresses the challenges in co-speech gesture generation, specifically the inability to produce synchronized, expressive motion in audio-driven videos. Current methods fall short due to the complexity of predicting full-body gestures, as the correlation between audio and body joints varies. The study hypothesizes that focusing on hand movements, which have a stronger correlation with audio, can improve gesture generation. Inspired by robotic control systems, the study considers hands as the "end-effectors" and proposes a focused approach to gesture generation.

Methodology

The research follows a two-stage method. In the first stage, hand poses are generated from audio data using a motion diffusion model, leveraging the correlation between audio signals and hand movements. The second stage uses a diffusion-based approach, employing a ReferenceNet backbone to craft video frames with synthesized hand poses, realistic facial expressions, and body movements. The methods rely on structured neural architectures and are supported by large datasets for training and evaluation.

Key Findings

• The proposed method exhibited superior performance in generating expressive hand motions as compared to traditional SMPL-based methods.
• Metrics showed the model achieved higher diversity and better beat alignment with audio, highlighting the vividness and expressiveness of the generated gestures.
• Video outputs showed improved synchronization between audio and visual elements, with better image quality and more dynamic expressions than competitive approaches.

Implications and Contributions

The study advances audio-driven gesture generation by introducing a robust, two-stage framework that emphasizes the synchronization and fidelity of co-speech gestures. It contributes to both theoretical understandings and practical applications, impacting fields like animated content creation and communication technology. The approach bridges the gap in current methods by improving the linkage between audio and gestures, proposing a simplified yet effective model architecture.

Conclusion

The study concludes that focusing on hand motion as an "end-effector" significantly improves the quality of audio-driven video generation. Future work may explore integrating larger datasets and fine-tuning computational efficiency. Limitations include reliance on specific datasets, and potential improvements could entail adaptive learning models.

Glossary

1. Audio-Driven Video Generation: Creating video content, specifically facial and gesture animations, synchronized with audio input.
2. Diffusion Model: A statistical method employed to generate data by learning patterns and reverse-engineering from noisy data.
3. Inverse Kinematics (IK): A computational technique to determine joint angles needed to achieve a desired position for part of an articulated system.
4. SMPL: A 3D human pose and shape model that parameterizes the space of human body shapes and poses.
5. Fréchet Gesture Distance (FGD): A metric that quantifies the similarity or dissimilarity between the distributions of generated and ground truth gestures.
6. MANO: A hand model used to describe and control three-dimensional hand poses and gestures.
7. ReferenceNet: A network architecture used to merge reference images with video frames for consistent animation quality.