# Talk Like You: Imitating How You Speak in Real-Time Talking Head Generation

arXiv: https://arxiv.org/abs/2610.06658 · HTML: https://mlsanity.com/paper/2610.06658/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 46 references that are in the map.
- First posted on arXiv 5 Oct 2026.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control](https://mlsanity.com/paper/2407.03168/) (2024)
- [Let Them Talk: Audio-Driven Multi-Person Conversational Video Generation](https://mlsanity.com/paper/2505.22647/) (2025)
- [MuseTalk: Real-Time High-Fidelity Video Dubbing via Spatio-Temporal Sampling](https://mlsanity.com/paper/2410.10122/) (2024)
- [DreamTalk: When Emotional Talking Head Generation Meets Diffusion Probabilistic Models](https://mlsanity.com/paper/2312.09767/) (2023)
- [PC-Talk: Precise Facial Animation Control for Audio-Driven Talking Face Generation](https://mlsanity.com/paper/2503.14295/) (2025)
- [STAvatar: Soft Binding and Temporal Density Control for Monocular 3D Head Avatars Reconstruction](https://mlsanity.com/paper/2511.19854/) (2025)
- [The Devil Is in the Leakage: A Disentangled Dual-Purification Framework for High-Fidelity Hairstyle Transfer](https://mlsanity.com/paper/2607.11281/) (2026)
- [FaceEditTalker: Controllable Talking Head Generation with Facial Attribute Editing](https://mlsanity.com/paper/2505.22141/) (2025)
- [Towards Flexible, Natural, Efficient Interaction for Conversational Talking Face Generation](https://mlsanity.com/paper/2606.31088/) (2026)

## Abstract (arXiv)

In daily life, each person exhibits unique speaking habits, leading to subtle yet consistent lip-shape variations even when pronouncing the same word. Although recent talking head generation methods have achieved impressive visual fidelity and lip synchronization, they largely overlook user-specific customization, especially the motion patterns that characterize individual speaking habits. These habits are difficult to model and capture, as their motion patterns are highly fine-grained and often similar across individuals. As a result, many approaches produce overly uniform facial motions and fail to capture diverse, person-specific articulation patterns. To address this, we propose TalkLikeYou, an efficient framework that imitates how a target person speaks in talking head generation. Our method models habit in motion-space and achieves real-time performance through Flow Matching with only one sampling step during inference. We further adopt a two-stage imitation learning strategy to capture subtle distinctions between habits, allowing users to specify a target habit through either a preset style from the dataset or a reference video. In addition, we introduce a new metric PLAD that projects mouth motions onto representative articulation axes to evaluate imitation accuracy and generation diversity. Extensive experiments demonstrate that TalkLikeYou generates high-quality talking heads in real-time and significantly improves speaking habit imitation compared with prior methods. The code is available at: https://github.com/BQ-Wang0511/TalkLikeYou