Efficient Emotional Adaptation for Audio-Driven Talking-Head Generation (2309.04946v2)

Abstract: Audio-driven talking-head synthesis is a popular research topic for virtual human-related applications. However, the inflexibility and inefficiency of existing methods, which necessitate expensive end-to-end training to transfer emotions from guidance videos to talking-head predictions, are significant limitations. In this work, we propose the Emotional Adaptation for Audio-driven Talking-head (EAT) method, which transforms emotion-agnostic talking-head models into emotion-controllable ones in a cost-effective and efficient manner through parameter-efficient adaptations. Our approach utilizes a pretrained emotion-agnostic talking-head transformer and introduces three lightweight adaptations (the Deep Emotional Prompts, Emotional Deformation Network, and Emotional Adaptation Module) from different perspectives to enable precise and realistic emotion controls. Our experiments demonstrate that our approach achieves state-of-the-art performance on widely-used benchmarks, including LRW and MEAD. Additionally, our parameter-efficient adaptations exhibit remarkable generalization ability, even in scenarios where emotional training videos are scarce or nonexistent. Project website: https://yuangan.github.io/eat/

• The paper presents EAT, a parameter-efficient method that transforms emotion-agnostic talking-head models into emotion-controllable systems.
• It leverages novel components—Deep Emotional Prompts, EDN, and EAM—to precisely fine-tune 3D facial expressions based on audio input.
• Empirical evaluations demonstrate state-of-the-art performance with reduced computational cost and zero-shot capabilities for nuanced emotion synthesis.

Efficient Emotional Adaptation for Audio-Driven Talking-Head Generation

The research paper entitled "Efficient Emotional Adaptation for Audio-Driven Talking-Head Generation" introduces a novel method, EAT (Emotional Adaptation for Talking-head), that addresses challenges in generating emotional talking-head videos. This innovative approach focuses on transforming emotion-agnostic talking-head models into emotion-controllable ones efficiently, thereby broadening their applicability in virtual human-related domains such as digital animations, visual dubbing, and content creation.

At the heart of the proposed methodology lies the notion of parameter-efficient adaptations. These adaptations are integral to the EAT approach, minimizing the computational burden typically associated with transferring emotions to talking-head models. Notably, the three adaptations introduced are: Deep Emotional Prompts, an Emotional Deformation Network (EDN), and the Emotional Adaptation Module (EAM), each calibrated to fine-tune emotional expressions with precision and realism.

Key Contributions and Methodology

The paper underscores several advancements over traditional methods:

1. Enhanced 3D Latent Representations: The authors build upon latent keypoints representation to capture subtle expressions more effectively. This enhancement forges improvements in the natural representation of facial expressions, even in the less data-abundant emotional domain.
2. Audio-to-Expression Transformer (A2ET): This component leverages audio features to predict a synchronized 3D expression deformation sequence, bridging the gap between speech-driven facial articulation and emotion portrayal. This Transformer framework capitalizes on the strengths observed in other domains like NLP, adapting them to visual tasks and thus improving the temporal context capture in talking-head generation.
3. Lightweight Emotional Adaptation Modules: By incorporating the novel adaptations—Deep Emotional Prompts, EDN, and EAM—the EAT paradigm facilitates the incorporation of emotional expressions with minimal computational overhead. This efficient adaptation allows for swift transitions between generating neutral and emotional talking-heads, a leap forward in usability and flexibility for practical applications.
4. Zero-Shot Expression Editing: The framework's adaptability extends to zero-shot capabilities, where emotional expressions can be induced using text guidance, utilizing image-text models like CLIP. This feature is particularly noteworthy as it circumvents the necessity for extensive emotional training datasets, broadening potential applications.

Experimental Results

Empirical evaluations exhibit the EAT's ability to achieve state-of-the-art results across several benchmarks, notably outperforming existing methods such as GC-AVT and EAMM. Quantitative measures like PSNR, SSIM, SyncNet, and FID, as well as emotional classification accuracy, highlight the model's robust performance in both emotion-agnostic and emotion-specific contexts. Impressively, EAT demonstrates remarkable tuning efficiency, achieving competitive performance with limited data and significantly reduced training time.

Moreover, user studies have corroborated these findings across metrics like audio-visual synchronization, video quality, and emotion classification accuracy. The model's adeptness at producing coherent and visually appealing emotional expressions has been underscored, although some challenges remain, particularly in representing nuanced emotions such as fear.

Practical and Theoretical Implications

Practically, the efficacy and efficiency of EAT pave the way for its deployment in various media applications where emotional depth enhances user experience. Theoretically, the work contributes to understanding how large-scale pre-trained models can be effectively fine-tuned for nuanced tasks with minimal additional parameters.

Speculation on Future Developments

Looking forward, further enhancements could involve improving the representational capacity of the model to handle a broader spectrum of emotions seamlessly. Addressing limitations such as the diversity of available emotional training data and the alignment of synthetic emotional expressions with genuine human emotions remains a fertile ground for exploration. Continued developments in zero-shot learning paradigms could also alleviate data constraints and foster more generalized usage.

Overall, this paper enriches the ongoing discourse on talking-head generation by providing a computationally viable and effective methodology for emotion synthesis. The introduction of the EAT technique represents a significant advance towards more adaptable and nuanced virtual human interactions.