# Synthesizing Normalized Faces from Facial Identity Features

arXiv: https://arxiv.org/abs/1701.04851 · HTML: https://mlsanity.com/paper/1701.04851/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2017.

## Built on by (most cited first, 30 of 33)

- [EAMM: One-Shot Emotional Talking Face via Audio-Based Emotion-Aware Motion Model](https://mlsanity.com/paper/2205.15278/) (2022)
- [AvatarMe++: Facial Shape and BRDF Inference with Photorealistic Rendering-Aware GANs](https://mlsanity.com/paper/2112.05957/) (2021)
- [Learning Disentangled Avatars with Hybrid 3D Representations](https://mlsanity.com/paper/2309.06441/) (2023)
- [FFHQ-UV: Normalized Facial UV-Texture Dataset for 3D Face Reconstruction](https://mlsanity.com/paper/2211.13874/) (2022)
- [Normalized Avatar Synthesis Using StyleGAN and Perceptual Refinement](https://mlsanity.com/paper/2106.11423/) (2021)
- [Fast-GANFIT: Generative Adversarial Network for High Fidelity 3D Face Reconstruction](https://mlsanity.com/paper/2105.07474/) (2021)
- [Privacy-preserving Adversarial Facial Features](https://mlsanity.com/paper/2305.05391/) (2023)
- [Controllable Inversion of Black-Box Face Recognition Models via Diffusion](https://mlsanity.com/paper/2303.13006/) (2023)
- [Towards Protecting Face Embeddings in Mobile Face Verification Scenarios](https://mlsanity.com/paper/2110.00434/) (2021)
- [A cappella: Audio-visual Singing Voice Separation](https://mlsanity.com/paper/2104.09946/) (2021)
- [Biometric Template Protection for Neural-Network-based Face Recognition Systems: A Survey of Methods and Evaluation Techniques](https://mlsanity.com/paper/2110.05044/) (2021)
- [Tackling Dynamics in Federated Incremental Learning with Variational Embedding Rehearsal](https://mlsanity.com/paper/2110.09695/) (2021)
- [BareSkinNet: De-makeup and De-lighting via 3D Face Reconstruction](https://mlsanity.com/paper/2209.09029/) (2022)
- [Face Reconstruction from Face Embeddings using Adapter to a Face Foundation Model](https://mlsanity.com/paper/2411.03960/) (2024)
- [SlerpFace: Face Template Protection via Spherical Linear Interpolation](https://mlsanity.com/paper/2407.03043/) (2024)
- [DisCO: Portrait Distortion Correction with Perspective-Aware 3D GANs](https://mlsanity.com/paper/2302.12253/) (2023)
- [Generalizable Face Landmarking Guided by Conditional Face Warping](https://mlsanity.com/paper/2404.12322/) (2024)
- [Vec2Face-v2: Unveil Human Faces from their Blackbox Features via Attention-based Network in Face Recognition](https://mlsanity.com/paper/2209.04920/) (2022)
- [CLIP-FTI: Fine-Grained Face Template Inversion via CLIP-Driven Attribute Conditioning](https://mlsanity.com/paper/2512.15433/) (2025)
- [A Survey on Deep Multi-modal Learning for Body Language Recognition and Generation](https://mlsanity.com/paper/2308.08849/) (2023)
- [Reconstruct Face from Features Using GAN Generator as a Distribution Constraint](https://mlsanity.com/paper/2206.04295/) (2022)
- [Privacy-Preserving Face Recognition in Hybrid Frequency-Color Domain](https://mlsanity.com/paper/2401.13386/) (2024)
- [Approximating Optimal Morphing Attacks using Template Inversion](https://mlsanity.com/paper/2402.00695/) (2024)
- [Fingerprint Template Invertibility: Minutiae vs. Deep Templates](https://mlsanity.com/paper/2205.03809/) (2022)
- [ID2image: Leakage of non-ID information into face descriptors and inversion from descriptors to images](https://mlsanity.com/paper/2304.07522/) (2023)
- [A Deeper Dive into the Irreversibility of PolyProtect: Making Protected Face Templates Harder to Invert](https://mlsanity.com/paper/2605.03857/) (2026)
- [Analysis and evaluation of Deep Learning based Super-Resolution algorithms to improve performance in Low-Resolution Face Recognition](https://mlsanity.com/paper/2101.10845/) (2021)
- [Profile to Frontal Face Recognition in the Wild Using Coupled Conditional GAN](https://mlsanity.com/paper/2107.13742/) (2021)
- [Non-Parametric Neural Style Transfer](https://mlsanity.com/paper/2108.12847/) (2021)
- [AdvBiom: Adversarial Attacks on Biometric Matchers](https://mlsanity.com/paper/2301.03966/) (2023)

## Abstract (arXiv)

We present a method for synthesizing a frontal, neutral-expression image of a person's face given an input face photograph. This is achieved by learning to generate facial landmarks and textures from features extracted from a facial-recognition network. Unlike previous approaches, our encoding feature vector is largely invariant to lighting, pose, and facial expression. Exploiting this invariance, we train our decoder network using only frontal, neutral-expression photographs. Since these photographs are well aligned, we can decompose them into a sparse set of landmark points and aligned texture maps. The decoder then predicts landmarks and textures independently and combines them using a differentiable image warping operation. The resulting images can be used for a number of applications, such as analyzing facial attributes, exposure and white balance adjustment, or creating a 3-D avatar.