# Mesh Guided One-shot Face Reenactment using Graph Convolutional Networks

arXiv: https://arxiv.org/abs/2008.07783 · HTML: https://mlsanity.com/paper/2008.07783/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2020.

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

- [Depth-Aware Generative Adversarial Network for Talking Head Video Generation](https://mlsanity.com/paper/2203.06605/) (2022)
- [EAMM: One-Shot Emotional Talking Face via Audio-Based Emotion-Aware Motion Model](https://mlsanity.com/paper/2205.15278/) (2022)
- [DINet: Deformation Inpainting Network for Realistic Face Visually Dubbing on High Resolution Video](https://mlsanity.com/paper/2303.03988/) (2023)
- [Implicit Identity Representation Conditioned Memory Compensation Network for Talking Head video Generation](https://mlsanity.com/paper/2307.09906/) (2023)
- [DPE: Disentanglement of Pose and Expression for General Video Portrait Editing](https://mlsanity.com/paper/2301.06281/) (2023)
- [MegActor: Harness the Power of Raw Video for Vivid Portrait Animation](https://mlsanity.com/paper/2405.20851/) (2024)
- [CVTHead: One-shot Controllable Head Avatar with Vertex-feature Transformer](https://mlsanity.com/paper/2311.06443/) (2023)
- [DaGAN++: Depth-Aware Generative Adversarial Network for Talking Head Video Generation](https://mlsanity.com/paper/2305.06225/) (2023)
- [Interactive Face Video Coding: A Generative Compression Framework](https://mlsanity.com/paper/2302.09919/) (2023)
- [FixTalk: Taming Identity Leakage for High-Quality Talking Head Generation in Extreme Cases](https://mlsanity.com/paper/2507.01390/) (2025)
- [One-shot Face Reenactment Using Appearance Adaptive Normalization](https://mlsanity.com/paper/2102.03984/) (2021)
- [Information-containing Adversarial Perturbation for Combating Facial Manipulation Systems](https://mlsanity.com/paper/2303.11625/) (2023)
- [From Pixels to Portraits: A Comprehensive Survey of Talking Head Generation Techniques and Applications](https://mlsanity.com/paper/2308.16041/) (2023)
- [Synergizing Motion and Appearance: Multi-Scale Compensatory Codebooks for Talking Head Video Generation](https://mlsanity.com/paper/2412.00719/) (2024)
- [Robust and Precise Facial Landmark Detection by Self-Calibrated Pose Attention Network](https://mlsanity.com/paper/2112.12328/) (2021)
- [One-shot Neural Face Reenactment via Finding Directions in GAN's Latent Space](https://mlsanity.com/paper/2402.03553/) (2024)
- [NTIRE 2025 XGC Quality Assessment Challenge: Methods and Results](https://mlsanity.com/paper/2506.02875/) (2025)
- [Advancing Talking Head Generation: A Comprehensive Survey of Multi-Modal Methodologies, Datasets, Evaluation Metrics, and Loss Functions](https://mlsanity.com/paper/2507.02900/) (2025)
- [Single Source One Shot Reenactment using Weighted motion From Paired Feature Points](https://mlsanity.com/paper/2104.03117/) (2021)
- [Spatial-Temporal Frequency Forgery Clue for Video Forgery Detection in VIS and NIR Scenario](https://mlsanity.com/paper/2207.01906/) (2022)
- [OSM-Net: One-to-Many One-shot Talking Head Generation with Spontaneous Head Motions](https://mlsanity.com/paper/2309.16148/) (2023)
- [A Comprehensive Taxonomy and Analysis of Talking Head Synthesis: Techniques for Portrait Generation, Driving Mechanisms, and Editing](https://mlsanity.com/paper/2406.10553/) (2024)
- [MaskRenderer: 3D-Infused Multi-Mask Realistic Face Reenactment](https://mlsanity.com/paper/2309.05095/) (2023)
- [Anchored Diffusion for Video Face Reenactment](https://mlsanity.com/paper/2407.15153/) (2024)
- [Learning Semantic Facial Descriptors for Accurate Face Animation](https://mlsanity.com/paper/2501.17718/) (2025)
- [Semantic-aware One-shot Face Re-enactment with Dense Correspondence Estimation](https://mlsanity.com/paper/2211.12674/) (2022)
- [Controllable One-Shot Face Video Synthesis With Semantic Aware Prior](https://mlsanity.com/paper/2304.14471/) (2023)
- [Learning Online Scale Transformation for Talking Head Video Generation](https://mlsanity.com/paper/2407.09965/) (2024)
- [PriorityCut: Occlusion-guided Regularization for Warp-based Image Animation](https://mlsanity.com/paper/2103.11600/) (2021)
- [FFAvatar: Feed-Forward 4D Head Avatar Reconstruction from Sparse Portrait Images](https://mlsanity.com/paper/2606.30347/) (2026)

## Abstract (arXiv)

Face reenactment aims to animate a source face image to a different pose and expression provided by a driving image. Existing approaches are either designed for a specific identity, or suffer from the identity preservation problem in the one-shot or few-shot scenarios. In this paper, we introduce a method for one-shot face reenactment, which uses the reconstructed 3D meshes (i.e., the source mesh and driving mesh) as guidance to learn the optical flow needed for the reenacted face synthesis. Technically, we explicitly exclude the driving face's identity information in the reconstructed driving mesh. In this way, our network can focus on the motion estimation for the source face without the interference of driving face shape. We propose a motion net to learn the face motion, which is an asymmetric autoencoder. The encoder is a graph convolutional network (GCN) that learns a latent motion vector from the meshes, and the decoder serves to produce an optical flow image from the latent vector with CNNs. Compared to previous methods using sparse keypoints to guide the optical flow learning, our motion net learns the optical flow directly from 3D dense meshes, which provide the detailed shape and pose information for the optical flow, so it can achieve more accurate expression and pose on the reenacted face. Extensive experiments show that our method can generate high-quality results and outperforms state-of-the-art methods in both qualitative and quantitative comparisons.