Cross-Modal Emotion Transfer for Emotion Editing in Talking Face Video
Abstract
Talking face generation has gained significant attention as a core application of generative models. To enhance the expressiveness and realism of synthesized videos, emotion editing in talking face video plays a crucial role. However, existing approaches often limit expressive flexibility and struggle to generate extended emotions. Label-based methods represent emotions with discrete categories, which fail to capture a wide range of emotions. Audio-based methods can leverage emotionally rich speech signals - and even benefit from expressive text-to-speech (TTS) synthesis - but they fail to express the target emotions because emotions and linguistic contents are entangled in emotional speeches. Images-based methods, on the other hand, rely on target reference images to guide emotion transfer, yet they require high-quality frontal views and face challenges in acquiring reference data for extended emotions (e.g., sarcasm). To address these limitations, we propose Cross-Modal Emotion Transfer (C-MET), a novel approach that generates facial expressions based on speeches by modeling emotion semantic vectors between speech and visual feature spaces. C-MET leverages a large-scale pretrained audio encoder and a disentangled facial expression encoder to learn emotion semantic vectors that represent the difference between two different emotional embeddings across modalities. Extensive experiments on the MEAD and CREMA-D datasets demonstrate that our method improves emotion accuracy by 14% over state-of-the-art methods, while generating expressive talking face videos - even for unseen extended emotions. Code, checkpoint, and demo are available at https://chanhyeok-choi.github.io/C-MET/
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents a novel approach to emotion editing in talking face videos through Cross-Modal Emotion Transfer (C-MET), significantly advancing the field by enabling the synthesis of extended emotions from audio inputs. The methodology is well-structured, and the experimental validation demonstrates its effectiveness, making it a valuable contribution to the machine learning community.
Methodology
The proposed Cross-Modal Emotion Transfer (C-MET) method is innovative in its approach to emotion editing in talking face videos by leveraging emotion semantic vectors derived from audio and visual modalities. The methodology effectively addresses the limitations of existing methods by enabling the generation of extended emotions without requiring extensive labeled datasets. The use of a contrastive learning framework to align audio and visual representations is a notable strength, as it enhances the model's ability to generalize across different emotional expressions.
Results & evaluation
The experiments conducted on the MEAD and CREMA-D datasets are comprehensive, demonstrating significant improvements in emotion accuracy over state-of-the-art methods. The quantitative metrics, such as \(Acc_{emo}\), alongside qualitative assessments from user studies, provide a robust evaluation of the model's performance. The results indicate that C-MET not only achieves higher accuracy but also maintains visual fidelity and synchronization, which are critical for practical applications.
Reproducibility
The paper includes sufficient implementation details, such as the choice of encoders, training protocols, and loss functions, which facilitate reproducibility. The availability of code and demo links further supports this aspect, although the actual code repository is not provided in the text.
Limitations
The model's reliance on a minimum number of speech samples for stable performance could limit its applicability in scenarios with limited data. Additionally, the current focus on English datasets restricts the model's generalizability to multilingual contexts. The inability to handle multi-view identity images is another notable limitation that could affect the model's robustness in diverse applications.
Broader impact
The ability to generate expressive talking face videos has significant implications for fields such as virtual reality, gaming, and telecommunication, where emotional engagement is crucial. The advancements in emotion editing can enhance human-computer interaction, making virtual agents more relatable and effective in applications like education and therapy. The potential for integrating this technology into various multimedia platforms could lead to more immersive and empathetic user experiences. The paper presents a novel approach to emotion editing in talking face videos through Cross-Modal Emotion Transfer (C-MET), significantly advancing the field by enabling the synthesis of extended emotions from audio inputs. The methodology is well-structured, and the experimental validation demonstrates its effectiveness, making it a valuable contribution to the machine learning community.
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