# Automatic Face Reenactment

arXiv: https://arxiv.org/abs/1602.02651 · HTML: https://mlsanity.com/paper/1602.02651/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2016.

## Built on by (most cited first, 30 of 43)

- [Neural Head Avatars from Monocular RGB Videos](https://mlsanity.com/paper/2112.01554/) (2021)
- [Towards Metrical Reconstruction of Human Faces](https://mlsanity.com/paper/2204.06607/) (2022)
- [Spatiotemporal Inconsistency Learning for DeepFake Video Detection](https://mlsanity.com/paper/2109.01860/) (2021)
- [A Latent Transformer for Disentangled Face Editing in Images and Videos](https://mlsanity.com/paper/2106.11895/) (2021)
- [HeadGaS: Real-Time Animatable Head Avatars via 3D Gaussian Splatting](https://mlsanity.com/paper/2312.02902/) (2023)
- [StyleAvatar: Real-time Photo-realistic Portrait Avatar from a Single Video](https://mlsanity.com/paper/2305.00942/) (2023)
- [Exploring Frequency Adversarial Attacks for Face Forgery Detection](https://mlsanity.com/paper/2203.15674/) (2022)
- [NeLF: Neural Light-transport Field for Portrait View Synthesis and Relighting](https://mlsanity.com/paper/2107.12351/) (2021)
- [Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectives](https://mlsanity.com/paper/2407.14962/) (2024)
- [Exploring Adversarial Fake Images on Face Manifold](https://mlsanity.com/paper/2101.03272/) (2021)
- [Deep Learning for Visual Speech Analysis: A Survey](https://mlsanity.com/paper/2205.10839/) (2022)
- [Sparse to Dense Dynamic 3D Facial Expression Generation](https://mlsanity.com/paper/2105.07463/) (2021)
- [Deepfake Video Detection with Spatiotemporal Dropout Transformer](https://mlsanity.com/paper/2207.06612/) (2022)
- [Tri2-plane: Thinking Head Avatar via Feature Pyramid](https://mlsanity.com/paper/2401.09386/) (2024)
- [Deepfake Forensics via An Adversarial Game](https://mlsanity.com/paper/2103.13567/) (2021)
- [UniFaceGAN: A Unified Framework for Temporally Consistent Facial Video Editing](https://mlsanity.com/paper/2108.05650/) (2021)
- [Everything's Talkin': Pareidolia Face Reenactment](https://mlsanity.com/paper/2104.03061/) (2021)
- [Egocentric Videoconferencing](https://mlsanity.com/paper/2107.03109/) (2021)
- [Free-HeadGAN: Neural Talking Head Synthesis with Explicit Gaze Control](https://mlsanity.com/paper/2208.02210/) (2022)
- [Audio-Driven Dubbing for User Generated Contents via Style-Aware Semi-Parametric Synthesis](https://mlsanity.com/paper/2309.00030/) (2023)
- [Cross-Modal Perceptionist: Can Face Geometry be Gleaned from Voices?](https://mlsanity.com/paper/2203.09824/) (2022)
- [Generating Multiple 4D Expression Transitions by Learning Face Landmark Trajectories](https://mlsanity.com/paper/2208.00050/) (2022)
- [Deep Semantic Manipulation of Facial Videos](https://mlsanity.com/paper/2111.07902/) (2021)
- [Neural 3D Clothes Retargeting from a Single Image](https://mlsanity.com/paper/2102.00062/) (2021)
- [Face2Parts: Exploring Coarse-to-Fine Inter-Regional Facial Dependencies for Generalized Deepfake Detection](https://mlsanity.com/paper/2603.26036/) (2026)
- [Voice2Mesh: Cross-Modal 3D Face Model Generation from Voices](https://mlsanity.com/paper/2104.10299/) (2021)
- [Feature Extraction Matters More: Universal Deepfake Disruption through Attacking Ensemble Feature Extractors](https://mlsanity.com/paper/2303.00200/) (2023)
- [BLiSS: Bootstrapped Linear Shape Space](https://mlsanity.com/paper/2309.01765/) (2023)
- [MI-NeRF: Learning a Single Face NeRF from Multiple Identities](https://mlsanity.com/paper/2403.19920/) (2024)
- [Impact of Facial Tattoos and Paintings on Face Recognition Systems](https://mlsanity.com/paper/2103.09939/) (2021)

## Abstract (arXiv)

We propose an image-based, facial reenactment system that replaces the face of an actor in an existing target video with the face of a user from a source video, while preserving the original target performance. Our system is fully automatic and does not require a database of source expressions. Instead, it is able to produce convincing reenactment results from a short source video captured with an off-the-shelf camera, such as a webcam, where the user performs arbitrary facial gestures. Our reenactment pipeline is conceived as part image retrieval and part face transfer: The image retrieval is based on temporal clustering of target frames and a novel image matching metric that combines appearance and motion to select candidate frames from the source video, while the face transfer uses a 2D warping strategy that preserves the user's identity. Our system excels in simplicity as it does not rely on a 3D face model, it is robust under head motion and does not require the source and target performance to be similar. We show convincing reenactment results for videos that we recorded ourselves and for low-quality footage taken from the Internet.