# EditEmoTalk: Controllable Speech-Driven 3D Facial Animation with Continuous Expression Editing

arXiv: https://arxiv.org/abs/2601.10000 · HTML: https://mlsanity.com/paper/2601.10000/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 41 references that are in the map.
- First posted on arXiv 15 Jan 2026.

## Builds on (most cited first, 24 of 24)

- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery](https://mlsanity.com/paper/2103.17249/) (2021)
- [Capture, Learning, and Synthesis of 3D Speaking Styles](https://mlsanity.com/paper/1905.03079/) (2019)
- [EMO: Emote Portrait Alive -- Generating Expressive Portrait Videos with Audio2Video Diffusion Model under Weak Conditions](https://mlsanity.com/paper/2402.17485/) (2024)
- [FaceFormer: Speech-Driven 3D Facial Animation with Transformers](https://mlsanity.com/paper/2112.05329/) (2021)
- [emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation](https://mlsanity.com/paper/2312.15185/) (2023)
- [MeshTalk: 3D Face Animation from Speech using Cross-Modality Disentanglement](https://mlsanity.com/paper/2104.08223/) (2021)
- [VASA-1: Lifelike Audio-Driven Talking Faces Generated in Real Time](https://mlsanity.com/paper/2404.10667/) (2024)
- [CodeTalker: Speech-Driven 3D Facial Animation with Discrete Motion Prior](https://mlsanity.com/paper/2301.02379/) (2023)
- [EmoTalk: Speech-Driven Emotional Disentanglement for 3D Face Animation](https://mlsanity.com/paper/2303.11089/) (2023)
- [Collaborative Diffusion for Multi-Modal Face Generation and Editing](https://mlsanity.com/paper/2304.10530/) (2023)
- [Emotional Speech-Driven Animation with Content-Emotion Disentanglement](https://mlsanity.com/paper/2306.08990/) (2023)
- [SMIRK: 3D Facial Expressions through Analysis-by-Neural-Synthesis](https://mlsanity.com/paper/2404.04104/) (2024)
- [DiffPoseTalk: Speech-Driven Stylistic 3D Facial Animation and Head Pose Generation via Diffusion Models](https://mlsanity.com/paper/2310.00434/) (2023)
- [FaceDiffuser: Speech-Driven 3D Facial Animation Synthesis Using Diffusion](https://mlsanity.com/paper/2309.11306/) (2023)
- [Imitator: Personalized Speech-driven 3D Facial Animation](https://mlsanity.com/paper/2301.00023/) (2022)
- [Disentangled Representation Learning for 3D Face Shape](https://mlsanity.com/paper/1902.09887/) (2019)
- [Face Generation and Editing with StyleGAN: A Survey](https://mlsanity.com/paper/2212.09102/) (2022)
- [RealTalk: Real-time and Realistic Audio-driven Face Generation with 3D Facial Prior-guided Identity Alignment Network](https://mlsanity.com/paper/2406.18284/) (2024)
- [DEEPTalk: Dynamic Emotion Embedding for Probabilistic Speech-Driven 3D Face Animation](https://mlsanity.com/paper/2408.06010/) (2024)
- [PESTalk: Speech-Driven 3D Facial Animation with Personalized Emotional Styles](https://mlsanity.com/paper/2512.05121/) (2025)
- [JambaTalk: Speech-Driven 3D Talking Head Generation Based on Hybrid Transformer-Mamba Model](https://mlsanity.com/paper/2408.01627/) (2024)
- [CSTalk: Correlation Supervised Speech-driven 3D Emotional Facial Animation Generation](https://mlsanity.com/paper/2404.18604/) (2024)

## Built on by (most cited first, 1 of 1)

- [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 aims to generate realistic and expressive facial motions directly from audio. While recent methods achieve high-quality lip synchronization, they often rely on discrete emotion categories, limiting continuous and fine-grained emotional control. We present EditEmoTalk, a controllable speech-driven 3D facial animation framework with continuous emotion editing. The key idea is a boundary-aware semantic embedding that learns the normal directions of inter-emotion decision boundaries, enabling a continuous expression manifold for smooth emotion manipulation. Moreover, we introduce an emotional consistency loss that enforces semantic alignment between the generated motion dynamics and the target emotion embedding through a mapping network, ensuring faithful emotional expression. Extensive experiments demonstrate that EditEmoTalk achieves superior controllability, expressiveness, and generalization while maintaining accurate lip synchronization. Code and pretrained models will be released.