EmoTalk: Speech-Driven Emotional Disentanglement for 3D Face Animation (2303.11089v2)

Abstract: Speech-driven 3D face animation aims to generate realistic facial expressions that match the speech content and emotion. However, existing methods often neglect emotional facial expressions or fail to disentangle them from speech content. To address this issue, this paper proposes an end-to-end neural network to disentangle different emotions in speech so as to generate rich 3D facial expressions. Specifically, we introduce the emotion disentangling encoder (EDE) to disentangle the emotion and content in the speech by cross-reconstructed speech signals with different emotion labels. Then an emotion-guided feature fusion decoder is employed to generate a 3D talking face with enhanced emotion. The decoder is driven by the disentangled identity, emotional, and content embeddings so as to generate controllable personal and emotional styles. Finally, considering the scarcity of the 3D emotional talking face data, we resort to the supervision of facial blendshapes, which enables the reconstruction of plausible 3D faces from 2D emotional data, and contribute a large-scale 3D emotional talking face dataset (3D-ETF) to train the network. Our experiments and user studies demonstrate that our approach outperforms state-of-the-art methods and exhibits more diverse facial movements. We recommend watching the supplementary video: https://ziqiaopeng.github.io/emotalk

Analysis of "EmoTalk: Speech-Driven Emotional Disentanglement for 3D Face Animation"

The paper "EmoTalk: Speech-Driven Emotional Disentanglement for 3D Face Animation" introduces a novel approach to enhancing the realism and emotion in 3D facial animation driven by speech input. The primary innovation lies in disentangling emotional content from speech while generating corresponding facial expressions, an area where prior methodologies have lagged.

Methodology Overview

The authors propose an end-to-end neural network architecture comprising two main components: an Emotion Disentangling Encoder (EDE) and an Emotion-Guided Feature Fusion Decoder. The EDE efficiently separates emotional information from speech content by leveraging cross-reconstruction and enabling distinct emotional embeddings. This disentangled representation addresses the challenge of accurately capturing emotional nuance—a limitation in existing techniques.

The decoder facilitates the integration of disentangled emotional and content features alongside adjustable parameters for personal style and emotional intensity. This component uses transformer-based mechanisms to output blendshape coefficients translating into expressive 3D facial movements. The versatility of the decoder enables user control over the animation's emotional and stylistic properties.

Dataset and Implementation

One of the critical challenges addressed in the paper is the scarcity of 3D emotional facial animation datasets. The authors contribute the 3D-ETF dataset, synthesized from existing 2D emotional audio-visual datasets using blendshape capture techniques refined by industry professionals. This dataset supports reliable training by providing approximate 3D facial expressions from 2D data, a noteworthy contribution to the field.

The model is trained utilizing various loss functions, including cross-reconstruction and velocity losses, ensuring the temporal coherence and emotional fidelity of generated animations. The incorporation of classification loss guides the emotional feature space differentiation, enhancing the model's capability to generate expressive animations accurately.

Evaluation and Results

The paper reports significant quantitative outcomes, with EmoTalk outperforming existing models such as VOCA and FaceFormer in Lip Vertex Error (LVE) and Emotional Vertex Error (EVE) metrics across multiple datasets, including RAVDESS and HDTF. Qualitative assessments emphasize EmoTalk's superior ability to produce expressive and synchronized facial animations that accurately translate speech-driven emotional cues.

EmoTalk's robustness is evident; it generalizes well to datasets it wasn't directly trained on, such as Voca-Test, where it demonstrated competitive performance against models tuned specifically on those datasets. User studies further corroborate the model's superior emotional expressiveness and lip synchronization, indicating a preference for EmoTalk over its predecessors in subjective evaluations.

Implications and Future Directions

EmoTalk sets a precedent in the field of speech-driven 3D animation by addressing key hurdles in emotion disentanglement and expressive facial rendering. Practically, this research could transform areas such as virtual reality, gaming, and digital avatars by providing more authentic emotional interactions. Theoretically, the work paves the way for further research into nuanced emotion representation and multimodal interaction modeling.

Future advancements may focus on enhancing real-time application feasibility by optimizing the computational overhead associated with disentanglement processes. Additionally, expanding dataset diversity and incorporating head and gaze dynamics could further enrich animation realism, bridging the gap between virtual and real-world expressiveness.

This paper marks a step forward in realistic 3D facial animation, where the emotion-driven paradigm offers fresh insights into speech-to-animation translation, paving the way for more immersive user experiences.