# One-shot Face Reenactment

arXiv: https://arxiv.org/abs/1908.03251 · HTML: https://mlsanity.com/paper/1908.03251/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2019.

## Built on by (most cited first, 21 of 21)

- [Neural Head Avatars from Monocular RGB Videos](https://mlsanity.com/paper/2112.01554/) (2021)
- [Depth-Aware Generative Adversarial Network for Talking Head Video Generation](https://mlsanity.com/paper/2203.06605/) (2022)
- [FACIAL: Synthesizing Dynamic Talking Face with Implicit Attribute Learning](https://mlsanity.com/paper/2108.07938/) (2021)
- [Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward](https://mlsanity.com/paper/2103.00484/) (2021)
- [Deepfake Detection: A Comprehensive Survey from the Reliability Perspective](https://mlsanity.com/paper/2211.10881/) (2022)
- [VLOGGER: Multimodal Diffusion for Embodied Avatar Synthesis](https://mlsanity.com/paper/2403.08764/) (2024)
- [High-Fidelity and Freely Controllable Talking Head Video Generation](https://mlsanity.com/paper/2304.10168/) (2023)
- [Audio-Driven Talking Face Video Generation with Dynamic Convolution Kernels](https://mlsanity.com/paper/2201.05986/) (2022)
- [DaGAN++: Depth-Aware Generative Adversarial Network for Talking Head Video Generation](https://mlsanity.com/paper/2305.06225/) (2023)
- [One-shot Face Reenactment Using Appearance Adaptive Normalization](https://mlsanity.com/paper/2102.03984/) (2021)
- [Learning Disentangled Representation for One-shot Progressive Face Swapping](https://mlsanity.com/paper/2203.12985/) (2022)
- [Metric Learning for Anti-Compression Facial Forgery Detection](https://mlsanity.com/paper/2103.08397/) (2021)
- [MusicFace: Music-driven Expressive Singing Face Synthesis](https://mlsanity.com/paper/2303.14044/) (2023)
- [Synthesizing Photorealistic Virtual Humans Through Cross-modal Disentanglement](https://mlsanity.com/paper/2209.01320/) (2022)
- [One-Shot Face Reenactment on Megapixels](https://mlsanity.com/paper/2205.13368/) (2022)
- [Robust Deepfake On Unrestricted Media: Generation And Detection](https://mlsanity.com/paper/2202.06228/) (2022)
- [Dual-MTGAN: Stochastic and Deterministic Motion Transfer for Image-to-Video Synthesis](https://mlsanity.com/paper/2102.13329/) (2021)
- [DeepFakes: Detecting Forged and Synthetic Media Content Using Machine Learning](https://mlsanity.com/paper/2109.02874/) (2021)
- [Semantic-aware One-shot Face Re-enactment with Dense Correspondence Estimation](https://mlsanity.com/paper/2211.12674/) (2022)
- [Solutions to Deepfakes: Can Camera Hardware, Cryptography, and Deep Learning Verify Real Images?](https://mlsanity.com/paper/2407.04169/) (2024)
- [3D Engine-ready Photorealistic Avatars via Dynamic Textures](https://mlsanity.com/paper/2503.14943/) (2025)

## Abstract (arXiv)

To enable realistic shape (e.g. pose and expression) transfer, existing face reenactment methods rely on a set of target faces for learning subject-specific traits. However, in real-world scenario end-users often only have one target face at hand, rendering existing methods inapplicable. In this work, we bridge this gap by proposing a novel one-shot face reenactment learning framework. Our key insight is that the one-shot learner should be able to disentangle and compose appearance and shape information for effective modeling. Specifically, the target face appearance and the source face shape are first projected into latent spaces with their corresponding encoders. Then these two latent spaces are associated by learning a shared decoder that aggregates multi-level features to produce the final reenactment results. To further improve the synthesizing quality on mustache and hair regions, we additionally propose FusionNet which combines the strengths of our learned decoder and the traditional warping method. Extensive experiments show that our one-shot face reenactment system achieves superior transfer fidelity as well as identity preserving capability than alternatives. More remarkably, our approach trained with only one target image per subject achieves competitive results to those using a set of target images, demonstrating the practical merit of this work. Code, models and an additional set of reenacted faces have been publicly released at the project page.