# Imitator: Personalized Speech-driven 3D Facial Animation

arXiv: https://arxiv.org/abs/2301.00023 · HTML: https://mlsanity.com/paper/2301.00023/

## Key facts

- Cited by 74 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 43 references that are in the map.
- First posted on arXiv 30 Dec 2022.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [FaceForensics++: Learning to Detect Manipulated Facial Images](https://mlsanity.com/paper/1901.08971/) (2019)
- [Face2Face: Real-time Face Capture and Reenactment of RGB Videos](https://mlsanity.com/paper/2007.14808/) (2020)
- [Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction](https://mlsanity.com/paper/2012.03065/) (2020)
- [Advances in Neural Rendering](https://mlsanity.com/paper/2111.05849/) (2021)
- [Deep Speech: Scaling up end-to-end speech recognition](https://mlsanity.com/paper/1412.5567/) (2014)
- [AD-NeRF: Audio Driven Neural Radiance Fields for Talking Head Synthesis](https://mlsanity.com/paper/2103.11078/) (2021)
- [MakeItTalk: Speaker-Aware Talking-Head Animation](https://mlsanity.com/paper/2004.12992/) (2020)
- [State of the Art on Neural Rendering](https://mlsanity.com/paper/2004.03805/) (2020)
- [3D Morphable Face Models -- Past, Present and Future](https://mlsanity.com/paper/1909.01815/) (2019)
- [Capture, Learning, and Synthesis of 3D Speaking Styles](https://mlsanity.com/paper/1905.03079/) (2019)
- [Neural Voice Puppetry: Audio-driven Facial Reenactment](https://mlsanity.com/paper/1912.05566/) (2019)
- [I M Avatar: Implicit Morphable Head Avatars from Videos](https://mlsanity.com/paper/2112.07471/) (2021)
- [FaceFormer: Speech-Driven 3D Facial Animation with Transformers](https://mlsanity.com/paper/2112.05329/) (2021)
- [MeshTalk: 3D Face Animation from Speech using Cross-Modality Disentanglement](https://mlsanity.com/paper/2104.08223/) (2021)
- [Towards Metrical Reconstruction of Human Faces](https://mlsanity.com/paper/2204.06607/) (2022)
- [Audio2Head: Audio-driven One-shot Talking-head Generation with Natural Head Motion](https://mlsanity.com/paper/2107.09293/) (2021)
- [Realistic Speech-Driven Facial Animation with GANs](https://mlsanity.com/paper/1906.06337/) (2019)
- [You said that?](https://mlsanity.com/paper/1705.02966/) (2017)
- [Audio-driven Talking Face Video Generation with Learning-based Personalized Head Pose](https://mlsanity.com/paper/2002.10137/) (2020)
- [Everybody's Talkin': Let Me Talk as You Want](https://mlsanity.com/paper/2001.05201/) (2020)
- [TorchAudio: Building Blocks for Audio and Speech Processing](https://mlsanity.com/paper/2110.15018/) (2021)
- [DFA-NeRF: Personalized Talking Head Generation via Disentangled Face Attributes Neural Rendering](https://mlsanity.com/paper/2201.00791/) (2022)
- [LipSync3D: Data-Efficient Learning of Personalized 3D Talking Faces from Video using Pose and Lighting Normalization](https://mlsanity.com/paper/2106.04185/) (2021)

## Built on by (most cited first, 30 of 74)

- [Emotional Speech-Driven Animation with Content-Emotion Disentanglement](https://mlsanity.com/paper/2306.08990/) (2023)
- [DiffPoseTalk: Speech-Driven Stylistic 3D Facial Animation and Head Pose Generation via Diffusion Models](https://mlsanity.com/paper/2310.00434/) (2023)
- [FaceTalk: Audio-Driven Motion Diffusion for Neural Parametric Head Models](https://mlsanity.com/paper/2312.08459/) (2023)
- [VLOGGER: Multimodal Diffusion for Embodied Avatar Synthesis](https://mlsanity.com/paper/2403.08764/) (2024)
- [Media2Face: Co-speech Facial Animation Generation With Multi-Modality Guidance](https://mlsanity.com/paper/2401.15687/) (2024)
- [DualTalk: Dual-Speaker Interaction for 3D Talking Head Conversations](https://mlsanity.com/paper/2505.18096/) (2025)
- [MultiTalk: Enhancing 3D Talking Head Generation Across Languages with Multilingual Video Dataset](https://mlsanity.com/paper/2406.14272/) (2024)
- [LaughTalk: Expressive 3D Talking Head Generation with Laughter](https://mlsanity.com/paper/2311.00994/) (2023)
- [ScanTalk: 3D Talking Heads from Unregistered Scans](https://mlsanity.com/paper/2403.10942/) (2024)
- [Gaussian Eigen Models for Human Heads](https://mlsanity.com/paper/2407.04545/) (2024)
- [Speech-Driven 3D Face Animation with Composite and Regional Facial Movements](https://mlsanity.com/paper/2308.05428/) (2023)
- [Mimic: Speaking Style Disentanglement for Speech-Driven 3D Facial Animation](https://mlsanity.com/paper/2312.10877/) (2023)
- [ReSyncer: Rewiring Style-based Generator for Unified Audio-Visually Synced Facial Performer](https://mlsanity.com/paper/2408.03284/) (2024)
- [MMHead: Towards Fine-grained Multi-modal 3D Facial Animation](https://mlsanity.com/paper/2410.07757/) (2024)
- [3DiFACE: Diffusion-based Speech-driven 3D Facial Animation and Editing](https://mlsanity.com/paper/2312.00870/) (2023)
- [EmoVOCA: Speech-Driven Emotional 3D Talking Heads](https://mlsanity.com/paper/2403.12886/) (2024)
- [ProbTalk3D: Non-Deterministic Emotion Controllable Speech-Driven 3D Facial Animation Synthesis Using VQ-VAE](https://mlsanity.com/paper/2409.07966/) (2024)
- [UniLS: End-to-End Audio-Driven Avatars for Unified Listening and Speaking](https://mlsanity.com/paper/2512.09327/) (2025)
- [DEGAS: Detailed Expressions on Full-Body Gaussian Avatars](https://mlsanity.com/paper/2408.10588/) (2024)
- [SingingHead: A Large-scale 4D Dataset for Singing Head Animation](https://mlsanity.com/paper/2312.04369/) (2023)
- [AVI-Talking: Learning Audio-Visual Instructions for Expressive 3D Talking Face Generation](https://mlsanity.com/paper/2402.16124/) (2024)
- [AV-Flow: Transforming Text to Audio-Visual Human-like Interactions](https://mlsanity.com/paper/2502.13133/) (2025)
- [MemoryTalker: Personalized Speech-Driven 3D Facial Animation via Audio-Guided Stylization](https://mlsanity.com/paper/2507.20562/) (2025)
- [Meta-Learning Empowered Meta-Face: Personalized Speaking Style Adaptation for Audio-Driven 3D Talking Face Animation](https://mlsanity.com/paper/2408.09357/) (2024)
- [Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven Facial Animation](https://mlsanity.com/paper/2505.23290/) (2025)
- [Model See Model Do: Speech-Driven Facial Animation with Style Control](https://mlsanity.com/paper/2505.01319/) (2025)
- [AnimateMe: 4D Facial Expressions via Diffusion Models](https://mlsanity.com/paper/2403.17213/) (2024)
- [Advancing Talking Head Generation: A Comprehensive Survey of Multi-Modal Methodologies, Datasets, Evaluation Metrics, and Loss Functions](https://mlsanity.com/paper/2507.02900/) (2025)
- [Beyond Fixed Topologies: Unregistered Training and Comprehensive Evaluation Metrics for 3D Talking Heads](https://mlsanity.com/paper/2410.11041/) (2024)
- [Towards High-fidelity 3D Talking Avatar with Personalized Dynamic Texture](https://mlsanity.com/paper/2503.00495/) (2025)

## Abstract (arXiv)

Speech-driven 3D facial animation has been widely explored, with applications in gaming, character animation, virtual reality, and telepresence systems. State-of-the-art methods deform the face topology of the target actor to sync the input audio without considering the identity-specific speaking style and facial idiosyncrasies of the target actor, thus, resulting in unrealistic and inaccurate lip movements. To address this, we present Imitator, a speech-driven facial expression synthesis method, which learns identity-specific details from a short input video and produces novel facial expressions matching the identity-specific speaking style and facial idiosyncrasies of the target actor. Specifically, we train a style-agnostic transformer on a large facial expression dataset which we use as a prior for audio-driven facial expressions. Based on this prior, we optimize for identity-specific speaking style based on a short reference video. To train the prior, we introduce a novel loss function based on detected bilabial consonants to ensure plausible lip closures and consequently improve the realism of the generated expressions. Through detailed experiments and a user study, we show that our approach produces temporally coherent facial expressions from input audio while preserving the speaking style of the target actors.