Source-linked AI summary
CETalk: Continuous Valence-Arousal Control for Audio-Driven 3D Talking Head Generation
Peng Jia, Li Dai, Zhen Xiao, Xueliang Liu, Jia Li
TL;DR
Emotion-aware 3D talking head generation needs continuous affect modeling and separate handling of fast articulation and slower emotional dynamics. CETalk addresses this with continuous Valence–Arousal control and multi-scale temporal modeling, achieving strong lip synchronization, facial motion accuracy, and emotion controllability.
Problem
Existing emotion-aware talking head methods often use discrete categories and do not adequately model the differing temporal scales of articulation and emotional expression.
Method
CETalk uses continuous Valence–Arousal conditioning, dynamic emotion modulation, multi-scale temporal modeling, adaptive fusion, and the 3D-VA-MEAD dataset.
Results
CETalk achieves strong performance in lip synchronization, facial motion accuracy, and continuous emotion controllability.
Takeaways & Limitations
Continuous affective modeling supports expressive, fine-grained, and temporally coherent 3D talking head animation beyond discrete emotion categories.
Abstract
from arXiv · showhide
Emotional 3D talking head generation aims to synthesize expressive facial animations with accurate lip synchronization. However, existing methods often rely on discrete emotion categories, which fail to capture the continuous evolution of affect. They also overlook the temporal frequency mismatch between audio articulation and emotional expression. In this paper, we propose CETalk, an audio-driven 3D facial animation framework conditioned on continuous Valence--Arousal (VA) representations for fine-grained emotion control. CETalk predicts a sequence of FLAME parameters through three key components: a Dynamic Emotion Modulation Module that adaptively scales emotional intensity using audio-derived cues; a Multi-Scale Temporal Modeling mechanism that employs parallel branches to decouple high-frequency articulatory movements from low-frequency emotional dynamics; and a Dynamic Fusion Mechanism that integrates these multi-scale features via an adaptive gating network. To support training and evaluation, we construct 3D-VA-MEAD, a large-scale dataset with automatically estimated VA annotations and reconstructed 3D facial motions. Extensive experiments demonstrate that CETalk outperforms state-of-the-art methods in both lip-sync accuracy and emotional expressiveness, while enabling smooth and controllable emotion transitions.
1 Introduction
CETalk addresses limitations of discrete emotion categories and mismatched temporal dynamics in audio-driven 3D talking-head generation through continuous Valence–Arousal conditioning and multi-scale modeling. The paper also introduces 3D-VA-MEAD with reconstructed 3D facial motion and frame-level VA annotations.
- Existing emotion-aware methods rely on discrete emotion categories, limiting subtle variation and smooth emotion transitions in conversational facial animation.
- Audio articulation requires high-frequency, frame-level synchronization, whereas emotional expressions evolve gradually at lower frequencies across broader facial regions.
- CETalk enables continuous emotion control through Valence–Arousal conditioning in an audio-driven 3D talking-head framework.
- Its Dynamic Emotion Modulation Module adaptively scales emotional intensity from speech cues, while Multi-Scale Temporal Modeling uses parallel high- and low-frequency branches.
- The 3D-VA-MEAD dataset provides reconstructed 3D facial motion and frame-level Valence–Arousal annotations for training and evaluation.
2 Related Work
Related work on audio-driven 3D talking heads progresses from audio-to-geometry mappings toward sequence modeling for long-range dependencies. Facial expressions are represented through discrete emotions, facial action units, or continuous affective models such as Valence–Arousal.
- Audio-driven 3D talking head generation: Early audio-driven 3D talking-head methods map audio features to facial geometry, including VOCA for 3D vertices and MeshTalk for disentangled facial dynamics.MeshTalk separates audio-related from audio-independent motions.
- Audio-driven 3D talking head generation: More recent approaches use sequence modeling to capture long-range dependencies, with FaceFormer employing an autoregressive Transformer.The supplied passage also identifies CodeTalker among recent sequence-modeling approaches, but its mechanism is not included.
- Facial expression representations: Facial expressions are commonly represented by discrete emotion categories, facial action units, or continuous affective models.These paradigms differ in how they encode emotional and facial-motion information.
- Facial expression representations: Discrete emotions are intuitive but miss subtle affective variations, whereas FACS offers fine-grained action-unit modeling but requires complex parameter specification.The Valence–Arousal framework instead represents emotions as points in a two-dimensional continuous space.
3 Method
CETalk maps audio, speaker identity, and continuous valence–arousal conditions to FLAME facial-motion sequences through dynamic emotion modulation, multi-scale temporal modeling, and adaptive fusion. Its training data combine automatically estimated, smoothed VA annotations with reconstructed and temporally smoothed 3D facial motions.
- Problem formulation: CETalk predicts FLAME facial-motion parameters from audio, speaker identity, and a frame-level valence–arousal condition.The target is a sequence P ∈ R^T × Dp, where Dp is the dimensionality of each FLAME parameter vector.
- 3D-VA-MEAD construction: 3D-VA-MEAD is built from MEAD using frame-level VA estimation, monocular FLAME reconstruction, and moving-average smoothing.The resulting smoothed sequence Econd supplies the continuous emotion condition for model training.
- Dynamic Emotion Modulation: The Dynamic Emotion Modulation Module converts static VA conditions into temporally varying affect using audio-derived scaling coefficients.Expression and audio–emotion encoders produce EVA and Eaudio, while M(Eaudio) predicts α ∈ R^T × 1 for element-wise modulation.
- Multi-Scale Temporal Modeling: Multi-Scale Temporal Modeling separates high-frequency articulatory motion from low-frequency emotional dynamics using parallel temporal branches.The HF branch uses speaker-conditioned cross-attention, whereas the LF branch downsamples audio and modulated emotion features, applies coarse-scale cross-attention, and upsamples the result.
- Dynamic Fusion Mechanism: A channel-wise gating network adaptively fuses high- and low-frequency features before predicting expression and jaw coefficients.The heads estimate Ψ ∈ R^T × 50 and Θ ∈ R^T × 3, concatenated into facial-motion parameters P ∈ R^T × 53.
- Training objectives: Training combines parameter, vertex, and velocity losses to reconstruct facial motion while promoting geometric consistency and temporal smoothness.Parameter loss uses L1 reconstruction, vertex loss constrains FLAME geometry, and velocity loss penalizes discrepancies between predicted and ground-truth vertex velocities.
4 Experiments
Experiments evaluate CETalk across multiple datasets using geometric and affective metrics, showing strong facial-motion accuracy, continuous emotion controllability, and expressive temporal dynamics. Ablations further identify dynamic emotion modulation and multi-scale temporal modeling as important components.
- Experimental Setup: CETalk is evaluated on MEAD, RAVDESS, and in-the-wild HDTF against six audio-driven 3D talking-head baselines.The baselines include FaceFormer, CodeTalker, EMOTE, ProbTalk3D, UniTalker, and DEEPTalk.
- Evaluation Metrics: The evaluation combines LVE, MVE, and EVE for facial motion with valence/arousal RMSE and SAGR for affective alignment.LVE measures lip-motion accuracy, MVE overall facial reconstruction error, EVE expression-region dynamics, RMSE affective deviation, and SAGR emotional-polarity consistency.
- Quantitative Results: On MEAD, CETalk achieves 7.4772 LVE and 9.9059 MVE, the lowest errors among the compared methods; on RAVDESS, it records 7.8145 LVE and 10.7293 MVE.On HDTF, the passage reports a further lowest-error result beginning with 4.7408, but the remaining metric value is truncated.
- Emotion Controllability: CETalk achieves the lowest RMSE and highest SAGR for both valence and arousal, indicating accurate affective trajectories and consistent emotional polarity.Affective signals are obtained by rendering predicted 3D animations into 2D images and applying a pretrained affect-estimation model.
- Qualitative Results: Qualitative results show accurate lip synchronization, richer and more coherent expressions, smoother transitions, and gradual VA-conditioned intensity changes while preserving lip-sync accuracy.Heatmaps also show closer agreement with ground-truth mean motion and more realistic temporal variation than the over-smoothed FaceFormer and CodeTalker outputs.
- Ablation Study: Removing DEMM increases EVE from 1.3256 to 2.4367, the largest reported degradation, while removing MSTM increases both LVE and EVE.These ablations indicate the importance of dynamic intensity modulation and explicit decoupling of articulatory and emotional dynamics.
5 Conclusion
CETalk introduces continuous Valence–Arousal control for temporally coherent, expressive 3D talking-head animation beyond discrete emotion categories. Its components and 3D-VA-MEAD dataset support strong lip synchronization, facial-motion accuracy, and emotion controllability.
- CETalk models emotion in a continuous Valence–Arousal space to enable fine-grained and temporally coherent facial animation beyond discrete emotion categories.
- CETalk combines dynamic emotion modulation, multi-scale temporal modeling, and adaptive feature fusion to capture audio-driven articulation and emotional dynamics jointly.
- The 3D-VA-MEAD dataset provides large-scale data for continuous-affect-driven 3D talking-head generation.
- CETalk achieves strong performance in lip synchronization, facial motion accuracy, and emotion controllability, validating continuous affective modeling for expressive 3D talking-head generation.