# DEEPTalk: Dynamic Emotion Embedding for Probabilistic Speech-Driven 3D Face Animation

arXiv: https://arxiv.org/abs/2408.06010 · HTML: https://mlsanity.com/paper/2408.06010/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 43 references that are in the map.
- First posted on arXiv 12 Aug 2024.

## Builds on (most cited first, 29 of 29)

- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Generating Diverse High-Fidelity Images with VQ-VAE-2](https://mlsanity.com/paper/1906.00446/) (2019)
- [AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild](https://mlsanity.com/paper/1708.03985/) (2017)
- [Capture, Learning, and Synthesis of 3D Speaking Styles](https://mlsanity.com/paper/1905.03079/) (2019)
- [EMOCA: Emotion Driven Monocular Face Capture and Animation](https://mlsanity.com/paper/2204.11312/) (2022)
- [emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation](https://mlsanity.com/paper/2312.15185/) (2023)
- [Speech Gesture Generation from the Trimodal Context of Text, Audio, and Speaker Identity](https://mlsanity.com/paper/2009.02119/) (2020)
- [MeshTalk: 3D Face Animation from Speech using Cross-Modality Disentanglement](https://mlsanity.com/paper/2104.08223/) (2021)
- [CodeTalker: Speech-Driven 3D Facial Animation with Discrete Motion Prior](https://mlsanity.com/paper/2301.02379/) (2023)
- [Audio-Driven Emotional Video Portraits](https://mlsanity.com/paper/2104.07452/) (2021)
- [Generating Holistic 3D Human Motion from Speech](https://mlsanity.com/paper/2212.04420/) (2022)
- [Probabilistic Embeddings for Cross-Modal Retrieval](https://mlsanity.com/paper/2101.05068/) (2021)
- [EAMM: One-Shot Emotional Talking Face via Audio-Based Emotion-Aware Motion Model](https://mlsanity.com/paper/2205.15278/) (2022)
- [EmoTalk: Speech-Driven Emotional Disentanglement for 3D Face Animation](https://mlsanity.com/paper/2303.11089/) (2023)
- [Learning the Best Pooling Strategy for Visual Semantic Embedding](https://mlsanity.com/paper/2011.04305/) (2020)
- [Learning to Listen: Modeling Non-Deterministic Dyadic Facial Motion](https://mlsanity.com/paper/2204.08451/) (2022)
- [Audio2Gestures: Generating Diverse Gestures from Speech Audio with Conditional Variational Autoencoders](https://mlsanity.com/paper/2108.06720/) (2021)
- [EMAGE: Towards Unified Holistic Co-Speech Gesture Generation via Expressive Masked Audio Gesture Modeling](https://mlsanity.com/paper/2401.00374/) (2023)
- [Emotional Speech-Driven Animation with Content-Emotion Disentanglement](https://mlsanity.com/paper/2306.08990/) (2023)
- [StyleTalk: One-shot Talking Head Generation with Controllable Speaking Styles](https://mlsanity.com/paper/2301.01081/) (2023)
- [Efficient Emotional Adaptation for Audio-Driven Talking-Head Generation](https://mlsanity.com/paper/2309.04946/) (2023)
- [FaceDiffuser: Speech-Driven 3D Facial Animation Synthesis Using Diffusion](https://mlsanity.com/paper/2309.11306/) (2023)
- [FaceTalk: Audio-Driven Motion Diffusion for Neural Parametric Head Models](https://mlsanity.com/paper/2312.08459/) (2023)
- [Diffusion Motion: Generate Text-Guided 3D Human Motion by Diffusion Model](https://mlsanity.com/paper/2210.12315/) (2022)
- [LaughTalk: Expressive 3D Talking Head Generation with Laughter](https://mlsanity.com/paper/2311.00994/) (2023)
- [Mimic: Speaking Style Disentanglement for Speech-Driven 3D Facial Animation](https://mlsanity.com/paper/2312.10877/) (2023)
- [Expressive Speech-driven Facial Animation with controllable emotions](https://mlsanity.com/paper/2301.02008/) (2023)

## Built on by (most cited first, 12 of 12)

- [ProbTalk3D: Non-Deterministic Emotion Controllable Speech-Driven 3D Facial Animation Synthesis Using VQ-VAE](https://mlsanity.com/paper/2409.07966/) (2024)
- [Think2Sing: Orchestrating Structured Motion Subtitles for Singing-Driven 3D Head Animation](https://mlsanity.com/paper/2509.02278/) (2025)
- [EmoDiffTalk:Emotion-aware Diffusion for Editable 3D Gaussian Talking Head](https://mlsanity.com/paper/2512.05991/) (2025)
- [EditEmoTalk: Controllable Speech-Driven 3D Facial Animation with Continuous Expression Editing](https://mlsanity.com/paper/2601.10000/) (2026)
- [3DXTalker: Unifying Identity, Lip Sync, Emotion, and Spatial Dynamics in Expressive 3D Talking Avatars](https://mlsanity.com/paper/2602.10516/) (2026)
- [FreeTalk: Emotional Topology-Free 3D Talking Heads](https://mlsanity.com/paper/2603.15512/) (2026)
- [PianoFlow: Music-Aware Streaming Piano Motion Generation with Bimanual Coordination](https://mlsanity.com/paper/2604.12856/) (2026)
- [EmpaAva: An Open-source Agentic 3D-Avatar Empathetic Live Chatbot](https://mlsanity.com/paper/2608.04709/) (2026)
- [SubtleTalk: Generating Controllable Weakly-correlated Facial Dynamics for 3D Talking Heads via Residual Flow Matching](https://mlsanity.com/paper/2608.06408/) (2026)
- [CETalk: Continuous Valence-Arousal Control for Audio-Driven 3D Talking Head Generation](https://mlsanity.com/paper/2608.15110/) (2026)
- [ECHO: Dyadic 3D Facial Motion Generation with Asymmetric Deterministic Articulation and Stochastic Reaction](https://mlsanity.com/paper/2609.05506/) (2026)
- [Seeing Speech: Learning Visible Articulatory Dynamics for Speech-Driven 3D Facial Animation](https://mlsanity.com/paper/2609.30517/) (2026)

## Abstract (arXiv)

Speech-driven 3D facial animation has garnered lots of attention thanks to its broad range of applications. Despite recent advancements in achieving realistic lip motion, current methods fail to capture the nuanced emotional undertones conveyed through speech and produce monotonous facial motion. These limitations result in blunt and repetitive facial animations, reducing user engagement and hindering their applicability. To address these challenges, we introduce DEEPTalk, a novel approach that generates diverse and emotionally rich 3D facial expressions directly from speech inputs. To achieve this, we first train DEE (Dynamic Emotion Embedding), which employs probabilistic contrastive learning to forge a joint emotion embedding space for both speech and facial motion. This probabilistic framework captures the uncertainty in interpreting emotions from speech and facial motion, enabling the derivation of emotion vectors from its multifaceted space. Moreover, to generate dynamic facial motion, we design TH-VQVAE (Temporally Hierarchical VQ-VAE) as an expressive and robust motion prior overcoming limitations of VAEs and VQ-VAEs. Utilizing these strong priors, we develop DEEPTalk, a talking head generator that non-autoregressively predicts codebook indices to create dynamic facial motion, incorporating a novel emotion consistency loss. Extensive experiments on various datasets demonstrate the effectiveness of our approach in creating diverse, emotionally expressive talking faces that maintain accurate lip-sync. Our project page is available at https://whwjdqls.github.io/deeptalkwebsite/