# Learning Residual Images for Face Attribute Manipulation

arXiv: https://arxiv.org/abs/1612.05363 · HTML: https://mlsanity.com/paper/1612.05363/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2016.

## Built on by (most cited first, 30 of 40)

- [VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization](https://mlsanity.com/paper/2103.16874/) (2021)
- [CelebV-HQ: A Large-Scale Video Facial Attributes Dataset](https://mlsanity.com/paper/2207.12393/) (2022)
- [A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?](https://mlsanity.com/paper/2303.11717/) (2023)
- [Deepfake Generation and Detection: A Benchmark and Survey](https://mlsanity.com/paper/2403.17881/) (2024)
- [FaceController: Controllable Attribute Editing for Face in the Wild](https://mlsanity.com/paper/2102.11464/) (2021)
- [DeepfakeUCL: Deepfake Detection via Unsupervised Contrastive Learning](https://mlsanity.com/paper/2104.11507/) (2021)
- [Learning Facial Representations from the Cycle-consistency of Face](https://mlsanity.com/paper/2108.03427/) (2021)
- [VecGAN: Image-to-Image Translation with Interpretable Latent Directions](https://mlsanity.com/paper/2207.03411/) (2022)
- [GAN-based Facial Attribute Manipulation](https://mlsanity.com/paper/2210.12683/) (2022)
- [A Review of the Role of Causality in Developing Trustworthy AI Systems](https://mlsanity.com/paper/2302.06975/) (2023)
- [Image-to-Image Translation with Disentangled Latent Vectors for Face Editing](https://mlsanity.com/paper/2301.04628/) (2023)
- [Disentangled Face Attribute Editing via Instance-Aware Latent Space Search](https://mlsanity.com/paper/2105.12660/) (2021)
- [Portrait Eyeglasses and Shadow Removal by Leveraging 3D Synthetic Data](https://mlsanity.com/paper/2203.10474/) (2022)
- [Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR Images](https://mlsanity.com/paper/2302.09200/) (2023)
- [Conditioning Diffusion Models via Attributes and Semantic Masks for Face Generation](https://mlsanity.com/paper/2306.00914/) (2023)
- [Reference-Based 3D-Aware Image Editing with Triplanes](https://mlsanity.com/paper/2404.03632/) (2024)
- [A Domain Gap Aware Generative Adversarial Network for Multi-domain Image Translation](https://mlsanity.com/paper/2110.10837/) (2021)
- [IA-FaceS: A Bidirectional Method for Semantic Face Editing](https://mlsanity.com/paper/2203.13097/) (2022)
- [Thinking Racial Bias in Fair Forgery Detection: Models, Datasets and Evaluations](https://mlsanity.com/paper/2407.14367/) (2024)
- [HealthyGAN: Learning from Unannotated Medical Images to Detect Anomalies Associated with Human Disease](https://mlsanity.com/paper/2209.01822/) (2022)
- [Stellar: Systematic Evaluation of Human-Centric Personalized Text-to-Image Methods](https://mlsanity.com/paper/2312.06116/) (2023)
- [US-GAN: On the importance of Ultimate Skip Connection for Facial Expression Synthesis](https://mlsanity.com/paper/2112.13002/) (2021)
- [Decouple-and-Sample: Protecting sensitive information in task agnostic data release](https://mlsanity.com/paper/2203.13204/) (2022)
- [SARGAN: Spatial Attention-based Residuals for Facial Expression Manipulation](https://mlsanity.com/paper/2303.17212/) (2023)
- [Impact of Sunglasses on One-to-Many Facial Identification Accuracy](https://mlsanity.com/paper/2412.05721/) (2024)
- [Mitigating Bias in Facial Analysis Systems by Incorporating Label Diversity](https://mlsanity.com/paper/2204.06364/) (2022)
- [Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection](https://mlsanity.com/paper/2212.05667/) (2022)
- [Motion Diffusion Autoencoders: Enabling Attribute Manipulation in Human Motion Demonstrated on Karate Techniques](https://mlsanity.com/paper/2501.18729/) (2025)
- [Disentangled representations: towards interpretation of sex determination from hip bone](https://mlsanity.com/paper/2112.09414/) (2021)
- [P-Age: Pexels Dataset for Robust Spatio-Temporal Apparent Age Classification](https://mlsanity.com/paper/2311.02432/) (2023)

## Abstract (arXiv)

Face attributes are interesting due to their detailed description of human faces. Unlike prior researches working on attribute prediction, we address an inverse and more challenging problem called face attribute manipulation which aims at modifying a face image according to a given attribute value. Instead of manipulating the whole image, we propose to learn the corresponding residual image defined as the difference between images before and after the manipulation. In this way, the manipulation can be operated efficiently with modest pixel modification. The framework of our approach is based on the Generative Adversarial Network. It consists of two image transformation networks and a discriminative network. The transformation networks are responsible for the attribute manipulation and its dual operation and the discriminative network is used to distinguish the generated images from real images. We also apply dual learning to allow transformation networks to learn from each other. Experiments show that residual images can be effectively learned and used for attribute manipulations. The generated images remain most of the details in attribute-irrelevant areas.