# Make a Face: Towards Arbitrary High Fidelity Face Manipulation

arXiv: https://arxiv.org/abs/1908.07191 · HTML: https://mlsanity.com/paper/1908.07191/

## Key facts

- Cited by 29 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 20 Aug 2019.

## Built on by (most cited first, 29 of 29)

- [MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model](https://mlsanity.com/paper/2311.16498/) (2023)
- [Motion Representations for Articulated Animation](https://mlsanity.com/paper/2104.11280/) (2021)
- [Controllable 3D Face Synthesis with Conditional Generative Occupancy Fields](https://mlsanity.com/paper/2206.08361/) (2022)
- [Perceptual Indistinguishability-Net (PI-Net): Facial Image Obfuscation with Manipulable Semantics](https://mlsanity.com/paper/2104.01753/) (2021)
- [CanonSwap: High-Fidelity and Consistent Video Face Swapping via Canonical Space Modulation](https://mlsanity.com/paper/2507.02691/) (2025)
- [Image-to-Video Generation via 3D Facial Dynamics](https://mlsanity.com/paper/2105.14678/) (2021)
- [Flow Guided Transformable Bottleneck Networks for Motion Retargeting](https://mlsanity.com/paper/2106.07771/) (2021)
- [IDOL: Unified Dual-Modal Latent Diffusion for Human-Centric Joint Video-Depth Generation](https://mlsanity.com/paper/2407.10937/) (2024)
- [Everything's Talkin': Pareidolia Face Reenactment](https://mlsanity.com/paper/2104.03061/) (2021)
- [Audio-Driven Dubbing for User Generated Contents via Style-Aware Semi-Parametric Synthesis](https://mlsanity.com/paper/2309.00030/) (2023)
- [What Makes for Good Tokenizers in Vision Transformer?](https://mlsanity.com/paper/2212.11115/) (2022)
- [Deep Image Synthesis from Intuitive User Input: A Review and Perspectives](https://mlsanity.com/paper/2107.04240/) (2021)
- [IA-FaceS: A Bidirectional Method for Semantic Face Editing](https://mlsanity.com/paper/2203.13097/) (2022)
- [DisCO: Portrait Distortion Correction with Perspective-Aware 3D GANs](https://mlsanity.com/paper/2302.12253/) (2023)
- [CUDA-GHR: Controllable Unsupervised Domain Adaptation for Gaze and Head Redirection](https://mlsanity.com/paper/2106.10852/) (2021)
- [PAGER: Progressive Attribute-Guided Extendable Robust Image Generation](https://mlsanity.com/paper/2206.00162/) (2022)
- [Heredity-aware Child Face Image Generation with Latent Space Disentanglement](https://mlsanity.com/paper/2108.11080/) (2021)
- [Differential Motion Evolution for Fine-Grained Motion Deformation in Unsupervised Image Animation](https://mlsanity.com/paper/2110.04658/) (2021)
- [Harnessing the Conditioning Sensorium for Improved Image Translation](https://mlsanity.com/paper/2110.06443/) (2021)
- [SeCGAN: Parallel Conditional Generative Adversarial Networks for Face Editing via Semantic Consistency](https://mlsanity.com/paper/2111.09298/) (2021)
- [Text to Image Synthesis using Stacked Conditional Variational Autoencoders and Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2207.03332/) (2022)
- [Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection](https://mlsanity.com/paper/2212.05667/) (2022)
- [PriorityCut: Occlusion-guided Regularization for Warp-based Image Animation](https://mlsanity.com/paper/2103.11600/) (2021)
- [Face Reconstruction with Variational Autoencoder and Face Masks](https://mlsanity.com/paper/2112.02139/) (2021)
- [Contextualize differential privacy in image database: a lightweight image differential privacy approach based on principle component analysis inverse](https://mlsanity.com/paper/2202.08309/) (2022)
- [CGOF++: Controllable 3D Face Synthesis with Conditional Generative Occupancy Fields](https://mlsanity.com/paper/2211.13251/) (2022)
- [Face Animation with Multiple Source Images](https://mlsanity.com/paper/2212.00256/) (2022)
- [DualVAE: Controlling Colours of Generated and Real Images](https://mlsanity.com/paper/2305.18769/) (2023)
- [Likelihood-Free Variational Autoencoders](https://mlsanity.com/paper/2504.17622/) (2025)

## Abstract (arXiv)

Recent studies have shown remarkable success in face manipulation task with the advance of GANs and VAEs paradigms, but the outputs are sometimes limited to low-resolution and lack of diversity. In this work, we propose Additive Focal Variational Auto-encoder (AF-VAE), a novel approach that can arbitrarily manipulate high-resolution face images using a simple yet effective model and only weak supervision of reconstruction and KL divergence losses. First, a novel additive Gaussian Mixture assumption is introduced with an unsupervised clustering mechanism in the structural latent space, which endows better disentanglement and boosts multi-modal representation with external memory. Second, to improve the perceptual quality of synthesized results, two simple strategies in architecture design are further tailored and discussed on the behavior of Human Visual System (HVS) for the first time, allowing for fine control over the model complexity and sample quality. Human opinion studies and new state-of-the-art Inception Score (IS) / Frechet Inception Distance (FID) demonstrate the superiority of our approach over existing algorithms, advancing both the fidelity and extremity of face manipulation task.