EMO2: End-Effector Guided Audio-Driven Avatar Video Generation (2501.10687v1)

Abstract: In this paper, we propose a novel audio-driven talking head method capable of simultaneously generating highly expressive facial expressions and hand gestures. Unlike existing methods that focus on generating full-body or half-body poses, we investigate the challenges of co-speech gesture generation and identify the weak correspondence between audio features and full-body gestures as a key limitation. To address this, we redefine the task as a two-stage process. In the first stage, we generate hand poses directly from audio input, leveraging the strong correlation between audio signals and hand movements. In the second stage, we employ a diffusion model to synthesize video frames, incorporating the hand poses generated in the first stage to produce realistic facial expressions and body movements. Our experimental results demonstrate that the proposed method outperforms state-of-the-art approaches, such as CyberHost and Vlogger, in terms of both visual quality and synchronization accuracy. This work provides a new perspective on audio-driven gesture generation and a robust framework for creating expressive and natural talking head animations.

• The paper proposes a two-stage framework that first generates hand poses from audio as end-effectors, then synthesizes video frames using these poses to enhance realistic co-speech gestures and facial expressions.
• Experimental results show EMO2 surpasses state-of-the-art methods in visual quality, beat alignment, and motion diversity for audio-driven avatar animation.
• This method enables more adaptable and responsive avatar systems for applications like virtual meetings and entertainment by bridging robotic control strategies with human motion synthesis.

Overview of "EMO2: End-Effector Guided Audio-Driven Avatar Video Generation"

The paper "EMO2: End-Effector Guided Audio-Driven Avatar Video Generation" introduces an advanced method for generating talking head animations from audio inputs by effectively capturing co-speech gestures and facial expressions. The authors address limitations in prior approaches that focus on full-body pose synthesis, proposing a two-stage framework to enhance visual quality and synchronization with audio.

Methodology

The authors articulate the synthesis process in two principal stages. The first stage involves generating hand poses directly from audio, leveraging the strong correlation between hand movements and sound, often more pronounced than with other body parts. This stage utilizes a diffusion model to predict realistic hand gestures that align with the given audio input. The novelty lies in treating hand poses as "end-effectors," akin to robotic systems where the end-effector's position dictates overall movement.

In the second stage, a video diffusion model synthesizes video frames incorporating the generated hand poses. This stage emphasizes the realistic portrayal of facial expressions and body movements, enhancing both the naturalness and expressiveness of audio-driven animations. The synthesis integrates the hand motion from the first stage with 2D generative models, which have innate knowledge of human body kinematics, referred to in the paper as "pixels prior IK". This approach ensures a coherent and natural representation of the persona in the video.

Results and Comparisons

The experimental evaluation demonstrates that the proposed EMO2 model surpasses various state-of-the-art methods such as CyberHost and Vlogger in terms of visual quality and synchronization with audio inputs. The evaluation metrics underscore improvements in beat alignment, motion diversity, and synchronization accuracy.

Quantitatively, the model achieves significantly higher diversity (DIV) and beat alignment (BA) scores, indicating enhanced expressiveness and temporal coherence with audio dynamics. Its approach to utilizing hand movements as primary control signals for body movement generation presents a striking improvement in handling complex co-speech scenarios, offering versatility across different contexts without loss of accuracy in representing body dynamics and synchronization.

Implications and Speculations

The introduction of end-effector guidance for audio-driven animation paves the way for more adaptable and responsive avatar systems in various applications. In practical terms, this methodology could be integrated into virtual meetings, entertainment, and social gaming, where realistic and expressive avatars are crucial. Theoretically, the paper sets a new direction in bridging robotic control strategies with human motion synthesis, potentially inspiring future research in hierarchical control systems in AI for audiovisual applications.

Future Directions

Given the promising results, the research opens avenues for deeper exploration into multi-modal input integration, robustness across diverse audio genres, and adaptation to unseen subjects. Further investigation can also explore fine-tuning models to capture micro-expressions and subtle gestures, enabling even richer interactions. The integration of larger datasets and advanced generative technologies could further refine the balance between authenticity and variability in avatar animations.

Overall, this work presents a substantial advance in the quest for natural, audio-responsive avatar animations, highlighting the synergies between structured motion generation models and creative AI frameworks.