# FaceTalk: Audio-Driven Motion Diffusion for Neural Parametric Head Models

arXiv: https://arxiv.org/abs/2312.08459 · HTML: https://mlsanity.com/paper/2312.08459/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- Builds on 71 of its 88 references that are in the map.
- First posted on arXiv 13 Dec 2023.

## Builds on (most cited first, 30 of 71)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2304.08818/) (2023)
- [DiffWave: A Versatile Diffusion Model for Audio Synthesis](https://mlsanity.com/paper/2009.09761/) (2020)
- [Efficient Geometry-aware 3D Generative Adversarial Networks](https://mlsanity.com/paper/2112.07945/) (2021)
- [Human Motion Diffusion Model](https://mlsanity.com/paper/2209.14916/) (2022)
- [Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning](https://mlsanity.com/paper/1702.03118/) (2017)
- [A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wild](https://mlsanity.com/paper/2008.10010/) (2020)
- [T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations](https://mlsanity.com/paper/2301.06052/) (2023)
- [Action-Conditioned 3D Human Motion Synthesis with Transformer VAE](https://mlsanity.com/paper/2104.05670/) (2021)
- [TEMOS: Generating diverse human motions from textual descriptions](https://mlsanity.com/paper/2204.14109/) (2022)
- [Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction](https://mlsanity.com/paper/2012.03065/) (2020)
- [Action2Motion: Conditioned Generation of 3D Human Motions](https://mlsanity.com/paper/2007.15240/) (2020)
- [AD-NeRF: Audio Driven Neural Radiance Fields for Talking Head Synthesis](https://mlsanity.com/paper/2103.11078/) (2021)
- [Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives](https://mlsanity.com/paper/2211.04894/) (2022)
- [MakeItTalk: Speaker-Aware Talking-Head Animation](https://mlsanity.com/paper/2004.12992/) (2020)
- [MCVD: Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation](https://mlsanity.com/paper/2205.09853/) (2022)
- [EDGE: Editable Dance Generation From Music](https://mlsanity.com/paper/2211.10658/) (2022)
- [Capture, Learning, and Synthesis of 3D Speaking Styles](https://mlsanity.com/paper/1905.03079/) (2019)
- [Language2Pose: Natural Language Grounded Pose Forecasting](https://mlsanity.com/paper/1907.01108/) (2019)
- [Hierarchical Cross-Modal Talking Face Generationwith Dynamic Pixel-Wise Loss](https://mlsanity.com/paper/1905.03820/) (2019)
- [Neural Voice Puppetry: Audio-driven Facial Reenactment](https://mlsanity.com/paper/1912.05566/) (2019)
- [FLAME: Free-form Language-based Motion Synthesis & Editing](https://mlsanity.com/paper/2209.00349/) (2022)
- [Talking Face Generation by Adversarially Disentangled Audio-Visual Representation](https://mlsanity.com/paper/1807.07860/) (2018)

## Built on by (most cited first, 30 of 55)

- [NPGA: Neural Parametric Gaussian Avatars](https://mlsanity.com/paper/2405.19331/) (2024)
- [Media2Face: Co-speech Facial Animation Generation With Multi-Modality Guidance](https://mlsanity.com/paper/2401.15687/) (2024)
- [ARTalk: Speech-Driven 3D Head Animation via Autoregressive Model](https://mlsanity.com/paper/2502.20323/) (2025)
- [Gaussian Eigen Models for Human Heads](https://mlsanity.com/paper/2407.04545/) (2024)
- [DiffSpeaker: Speech-Driven 3D Facial Animation with Diffusion Transformer](https://mlsanity.com/paper/2402.05712/) (2024)
- [MMHead: Towards Fine-grained Multi-modal 3D Facial Animation](https://mlsanity.com/paper/2410.07757/) (2024)
- [ProbTalk3D: Non-Deterministic Emotion Controllable Speech-Driven 3D Facial Animation Synthesis Using VQ-VAE](https://mlsanity.com/paper/2409.07966/) (2024)
- [DEEPTalk: Dynamic Emotion Embedding for Probabilistic Speech-Driven 3D Face Animation](https://mlsanity.com/paper/2408.06010/) (2024)
- [Cafe-Talk: Generating 3D Talking Face Animation with Multimodal Coarse- and Fine-grained Control](https://mlsanity.com/paper/2503.14517/) (2025)
- [DEGAS: Detailed Expressions on Full-Body Gaussian Avatars](https://mlsanity.com/paper/2408.10588/) (2024)
- [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)
- [Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven Facial Animation](https://mlsanity.com/paper/2505.23290/) (2025)
- [VGGSounder: Audio-Visual Evaluations for Foundation Models](https://mlsanity.com/paper/2508.08237/) (2025)
- [Audio2Face-3D: Audio-driven Realistic Facial Animation For Digital Avatars](https://mlsanity.com/paper/2508.16401/) (2025)
- [Audio-Driven Universal Gaussian Head Avatars](https://mlsanity.com/paper/2509.18924/) (2025)
- [AnimateMe: 4D Facial Expressions via Diffusion Models](https://mlsanity.com/paper/2403.17213/) (2024)
- [Combo: Co-speech holistic 3D human motion generation and efficient customizable adaptation in harmony](https://mlsanity.com/paper/2408.09397/) (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)
- [Supervising 3D Talking Head Avatars with Analysis-by-Audio-Synthesis](https://mlsanity.com/paper/2504.13386/) (2025)
- [Audio Driven Real-Time Facial Animation for Social Telepresence](https://mlsanity.com/paper/2510.01176/) (2025)
- [FreeAvatar: Robust 3D Facial Animation Transfer by Learning an Expression Foundation Model](https://mlsanity.com/paper/2409.13180/) (2024)
- [Passive Deepfake Detection Across Multi-modalities: A Comprehensive Survey](https://mlsanity.com/paper/2411.17911/) (2024)
- [Exploring Timeline Control for Facial Motion Generation](https://mlsanity.com/paper/2505.20861/) (2025)
- [3DiFACE: Synthesizing and Editing Holistic 3D Facial Animation](https://mlsanity.com/paper/2509.26233/) (2025)
- [FD2Talk: Towards Generalized Talking Head Generation with Facial Decoupled Diffusion Model](https://mlsanity.com/paper/2408.09384/) (2024)
- [Learning Phonetic Context-Dependent Viseme for Enhancing Speech-Driven 3D Facial Animation](https://mlsanity.com/paper/2507.20568/) (2025)

## Abstract (arXiv)

We introduce FaceTalk, a novel generative approach designed for synthesizing high-fidelity 3D motion sequences of talking human heads from input audio signal. To capture the expressive, detailed nature of human heads, including hair, ears, and finer-scale eye movements, we propose to couple speech signal with the latent space of neural parametric head models to create high-fidelity, temporally coherent motion sequences. We propose a new latent diffusion model for this task, operating in the expression space of neural parametric head models, to synthesize audio-driven realistic head sequences. In the absence of a dataset with corresponding NPHM expressions to audio, we optimize for these correspondences to produce a dataset of temporally-optimized NPHM expressions fit to audio-video recordings of people talking. To the best of our knowledge, this is the first work to propose a generative approach for realistic and high-quality motion synthesis of volumetric human heads, representing a significant advancement in the field of audio-driven 3D animation. Notably, our approach stands out in its ability to generate plausible motion sequences that can produce high-fidelity head animation coupled with the NPHM shape space. Our experimental results substantiate the effectiveness of FaceTalk, consistently achieving superior and visually natural motion, encompassing diverse facial expressions and styles, outperforming existing methods by 75% in perceptual user study evaluation.