# PC-Talk: Precise Facial Animation Control for Audio-Driven Talking Face Generation

arXiv: https://arxiv.org/abs/2503.14295 · HTML: https://mlsanity.com/paper/2503.14295/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 54 references that are in the map.
- First posted on arXiv 18 Mar 2025.

## Builds on (most cited first, 30 of 35)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [First Order Motion Model for Image Animation](https://mlsanity.com/paper/2003.00196/) (2020)
- [A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wild](https://mlsanity.com/paper/2008.10010/) (2020)
- [Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set](https://mlsanity.com/paper/1903.08527/) (2019)
- [One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing](https://mlsanity.com/paper/2011.15126/) (2020)
- [SadTalker: Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation](https://mlsanity.com/paper/2211.12194/) (2022)
- [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)
- [LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control](https://mlsanity.com/paper/2407.03168/) (2024)
- [EchoMimic: Lifelike Audio-Driven Portrait Animations through Editable Landmark Conditions](https://mlsanity.com/paper/2407.08136/) (2024)
- [AniPortrait: Audio-Driven Synthesis of Photorealistic Portrait Animation](https://mlsanity.com/paper/2403.17694/) (2024)
- [VASA-1: Lifelike Audio-Driven Talking Faces Generated in Real Time](https://mlsanity.com/paper/2404.10667/) (2024)
- [Follow-Your-Emoji: Fine-Controllable and Expressive Freestyle Portrait Animation](https://mlsanity.com/paper/2406.01900/) (2024)
- [EAMM: One-Shot Emotional Talking Face via Audio-Based Emotion-Aware Motion Model](https://mlsanity.com/paper/2205.15278/) (2022)
- [VideoReTalking: Audio-based Lip Synchronization for Talking Head Video Editing In the Wild](https://mlsanity.com/paper/2211.14758/) (2022)
- [Identity-Preserving Talking Face Generation with Landmark and Appearance Priors](https://mlsanity.com/paper/2305.08293/) (2023)
- [Hallo2: Long-Duration and High-Resolution Audio-Driven Portrait Image Animation](https://mlsanity.com/paper/2410.07718/) (2024)
- [StyleTalk: One-shot Talking Head Generation with Controllable Speaking Styles](https://mlsanity.com/paper/2301.01081/) (2023)
- [StyleSync: High-Fidelity Generalized and Personalized Lip Sync in Style-based Generator](https://mlsanity.com/paper/2305.05445/) (2023)
- [Efficient Emotional Adaptation for Audio-Driven Talking-Head Generation](https://mlsanity.com/paper/2309.04946/) (2023)
- [EDTalk: Efficient Disentanglement for Emotional Talking Head Synthesis](https://mlsanity.com/paper/2404.01647/) (2024)
- [DINet: Deformation Inpainting Network for Realistic Face Visually Dubbing on High Resolution Video](https://mlsanity.com/paper/2303.03988/) (2023)
- [Sonic: Shifting Focus to Global Audio Perception in Portrait Animation](https://mlsanity.com/paper/2411.16331/) (2024)
- [EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTa](https://mlsanity.com/paper/2108.12009/) (2021)
- [Frame attention networks for facial expression recognition in videos](https://mlsanity.com/paper/1907.00193/) (2019)

## Built on by (most cited first, 6 of 6)

- [STAvatar: Soft Binding and Temporal Density Control for Monocular 3D Head Avatars Reconstruction](https://mlsanity.com/paper/2511.19854/) (2025)
- [PortraitDirector: A Hierarchical Disentanglement Framework for Controllable and Real-time Facial Reenactment](https://mlsanity.com/paper/2604.19129/) (2026)
- [Towards Flexible, Natural, Efficient Interaction for Conversational Talking Face Generation](https://mlsanity.com/paper/2606.31088/) (2026)
- [Knowing You at First Glance: Inferring Apparent Personality from Faces](https://mlsanity.com/paper/2607.14631/) (2026)
- [Xemo-Talker: Unlock Emotions Explicitly for Audio-Driven Talking Portrait Synthesis](https://mlsanity.com/paper/2608.14700/) (2026)
- [Talk Like You: Imitating How You Speak in Real-Time Talking Head Generation](https://mlsanity.com/paper/2610.06658/) (2026)

## Abstract (arXiv)

Recent advancements in audio-driven talking face generation have made great progress in lip synchronization. However, current methods often lack sufficient control over facial animation such as speaking style and emotional expression, resulting in uniform outputs. In this paper, we focus on improving two key factors: lip-audio alignment and emotion control, to enhance the diversity and user-friendliness of talking videos. Lip-audio alignment control focuses on elements like speaking style and the scale of lip movements, whereas emotion control is centered on generating realistic emotional expressions, allowing for modifications in multiple attributes such as intensity. To achieve precise control of facial animation, we propose a novel framework, PC-Talk, which enables lip-audio alignment and emotion control through implicit keypoint deformations. First, our lip-audio alignment control module facilitates precise editing of speaking styles at the word level and adjusts lip movement scales to simulate varying vocal loudness levels, maintaining lip synchronization with the audio. Second, our emotion control module generates vivid emotional facial features with pure emotional deformation. This module also enables the fine modification of intensity and the combination of multiple emotions across different facial regions. Our method demonstrates outstanding control capabilities and achieves state-of-the-art performance on both HDTF and MEAD datasets in extensive experiments.