# Say Anything with Any Style

arXiv: https://arxiv.org/abs/2403.06363 · HTML: https://mlsanity.com/paper/2403.06363/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 54 references that are in the map.
- First posted on arXiv 11 Mar 2024.

## Builds on (most cited first, 30 of 33)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [HyperNetworks](https://mlsanity.com/paper/1609.09106/) (2016)
- [A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wild](https://mlsanity.com/paper/2008.10010/) (2020)
- [Learning multiple visual domains with residual adapters](https://mlsanity.com/paper/1705.08045/) (2017)
- [Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set](https://mlsanity.com/paper/1903.08527/) (2019)
- [SadTalker: Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation](https://mlsanity.com/paper/2211.12194/) (2022)
- [Dynamic Convolution: Attention over Convolution Kernels](https://mlsanity.com/paper/1912.03458/) (2019)
- [MakeItTalk: Speaker-Aware Talking-Head Animation](https://mlsanity.com/paper/2004.12992/) (2020)
- [Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation](https://mlsanity.com/paper/2104.11116/) (2021)
- [Hierarchical Cross-Modal Talking Face Generationwith Dynamic Pixel-Wise Loss](https://mlsanity.com/paper/1905.03820/) (2019)
- [Talking Face Generation by Adversarially Disentangled Audio-Visual Representation](https://mlsanity.com/paper/1807.07860/) (2018)
- [PIRenderer: Controllable Portrait Image Generation via Semantic Neural Rendering](https://mlsanity.com/paper/2109.08379/) (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)
- [EAMM: One-Shot Emotional Talking Face via Audio-Based Emotion-Aware Motion Model](https://mlsanity.com/paper/2205.15278/) (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)
- [Learning to Listen: Modeling Non-Deterministic Dyadic Facial Motion](https://mlsanity.com/paper/2204.08451/) (2022)
- [Talking-head Generation with Rhythmic Head Motion](https://mlsanity.com/paper/2007.08547/) (2020)
- [StyleTalk: One-shot Talking Head Generation with Controllable Speaking Styles](https://mlsanity.com/paper/2301.01081/) (2023)
- [One-shot Talking Face Generation from Single-speaker Audio-Visual Correlation Learning](https://mlsanity.com/paper/2112.02749/) (2021)
- [DINet: Deformation Inpainting Network for Realistic Face Visually Dubbing on High Resolution Video](https://mlsanity.com/paper/2303.03988/) (2023)
- [SyncTalkFace: Talking Face Generation with Precise Lip-Syncing via Audio-Lip Memory](https://mlsanity.com/paper/2211.00924/) (2022)
- [The challenge of realistic music generation: modelling raw audio at scale](https://mlsanity.com/paper/1806.10474/) (2018)
- [Write-a-speaker: Text-based Emotional and Rhythmic Talking-head Generation](https://mlsanity.com/paper/2104.07995/) (2021)
- [Emotion-Controllable Generalized Talking Face Generation](https://mlsanity.com/paper/2205.01155/) (2022)
- [Self-attention encoding and pooling for speaker recognition](https://mlsanity.com/paper/2008.01077/) (2020)

## Built on by (most cited first, 26 of 26)

- [Animate-X: Universal Character Image Animation with Enhanced Motion Representation](https://mlsanity.com/paper/2410.10306/) (2024)
- [EDTalk: Efficient Disentanglement for Emotional Talking Head Synthesis](https://mlsanity.com/paper/2404.01647/) (2024)
- [Ditto: Motion-Space Diffusion for Controllable Realtime Talking Head Synthesis](https://mlsanity.com/paper/2411.19509/) (2024)
- [ARTalk: Speech-Driven 3D Head Animation via Autoregressive Model](https://mlsanity.com/paper/2502.20323/) (2025)
- [MegActor: Harness the Power of Raw Video for Vivid Portrait Animation](https://mlsanity.com/paper/2405.20851/) (2024)
- [Mimir: Improving Video Diffusion Models for Precise Text Understanding](https://mlsanity.com/paper/2412.03085/) (2024)
- [FixTalk: Taming Identity Leakage for High-Quality Talking Head Generation in Extreme Cases](https://mlsanity.com/paper/2507.01390/) (2025)
- [Animate-X++: Universal Character Image Animation with Dynamic Backgrounds](https://mlsanity.com/paper/2508.09454/) (2025)
- [DH-FaceVid-1K: A Large-Scale High-Quality Dataset for Face Video Generation](https://mlsanity.com/paper/2410.07151/) (2024)
- [SynMotion: Semantic-Visual Adaptation for Motion Customized Video Generation](https://mlsanity.com/paper/2506.23690/) (2025)
- [PC-Talk: Precise Facial Animation Control for Audio-Driven Talking Face Generation](https://mlsanity.com/paper/2503.14295/) (2025)
- [MEDTalk: Multimodal Controlled 3D Facial Animation with Dynamic Emotions by Disentangled Embedding](https://mlsanity.com/paper/2507.06071/) (2025)
- [Style-Preserving Lip Sync via Audio-Aware Style Reference](https://mlsanity.com/paper/2408.05412/) (2024)
- [PortraitTalk: Towards Customizable One-Shot Audio-to-Talking Face Generation](https://mlsanity.com/paper/2412.07754/) (2024)
- [EDTalk++: Full Disentanglement for Controllable Talking Head Synthesis](https://mlsanity.com/paper/2508.13442/) (2025)
- [JambaTalk: Speech-Driven 3D Talking Head Generation Based on Hybrid Transformer-Mamba Model](https://mlsanity.com/paper/2408.01627/) (2024)
- [Identity-Preserving Video Dubbing Using Motion Warping](https://mlsanity.com/paper/2501.04586/) (2025)
- [DiTalker: A Unified DiT-based Framework for High-Quality and Speaking Styles Controllable Portrait Animation](https://mlsanity.com/paper/2508.06511/) (2025)
- [CoDance: An Unbind-Rebind Paradigm for Robust Multi-Subject Animation](https://mlsanity.com/paper/2601.11096/) (2026)
- [EmoTaG: Emotion-Aware Talking Head Synthesis on Gaussian Splatting with Few-Shot Personalization](https://mlsanity.com/paper/2603.21332/) (2026)
- [SpA2V: Harnessing Spatial Auditory Cues for Audio-driven Spatially-aware Video Generation](https://mlsanity.com/paper/2508.00782/) (2025)
- [Unmasking Puppeteers: Leveraging Biometric Leakage to Expose Impersonation in AI-Based Videoconferencing](https://mlsanity.com/paper/2510.03548/) (2025)
- [ESGaussianFace: Emotional and Stylized Audio-Driven Facial Animation via 3D Gaussian Splatting](https://mlsanity.com/paper/2601.01847/) (2026)
- [AUHead: Realistic Emotional Talking Head Generation via Action Units Control](https://mlsanity.com/paper/2602.09534/) (2026)
- [OpFlowTalker: Realistic and Natural Talking Face Generation via Optical Flow Guidance](https://mlsanity.com/paper/2405.14709/) (2024)
- [CapTalk: Text-Guided Stylization and Speech-Driven 3D Head Animation](https://mlsanity.com/paper/2605.29316/) (2026)

## Abstract (arXiv)

Generating stylized talking head with diverse head motions is crucial for achieving natural-looking videos but still remains challenging. Previous works either adopt a regressive method to capture the speaking style, resulting in a coarse style that is averaged across all training data, or employ a universal network to synthesize videos with different styles which causes suboptimal performance. To address these, we propose a novel dynamic-weight method, namely Say Anything withAny Style (SAAS), which queries the discrete style representation via a generative model with a learned style codebook. Specifically, we develop a multi-task VQ-VAE that incorporates three closely related tasks to learn a style codebook as a prior for style extraction. This discrete prior, along with the generative model, enhances the precision and robustness when extracting the speaking styles of the given style clips. By utilizing the extracted style, a residual architecture comprising a canonical branch and style-specific branch is employed to predict the mouth shapes conditioned on any driving audio while transferring the speaking style from the source to any desired one. To adapt to different speaking styles, we steer clear of employing a universal network by exploring an elaborate HyperStyle to produce the style-specific weights offset for the style branch. Furthermore, we construct a pose generator and a pose codebook to store the quantized pose representation, allowing us to sample diverse head motions aligned with the audio and the extracted style. Experiments demonstrate that our approach surpasses state-of-theart methods in terms of both lip-synchronization and stylized expression. Besides, we extend our SAAS to video-driven style editing field and achieve satisfactory performance.