A Large-scale Evaluation of Text-guided Models for Facial Editing
First large-scale evaluation of text-guided models like Nano Banana for facial editing, assessing ~1M images.
Key Findings
Methodology
The study evaluates six text-guided models for facial editing, including Nano Banana and Flux-Dev. It uses the Face-Edit-Attributes dataset, covering 169 facial editing attributes. Models are assessed using CelebA and CelebSET datasets, focusing on hair, accessory, and pose edits.
Key Results
- Result 1: Models performed well on hair and accessory edits but struggled with pose edits.
- Result 2: All models exhibited overediting, particularly for dark-skinned male and old faces.
- Result 3: Qwen-Edit and Flux-Dev showed consistent gender and skin-tone bias in identity preservation and edit execution.
Significance
This study provides the first systematic evaluation of text-guided models for facial editing, revealing biases in handling different skin tones and ages, offering crucial insights for future fairness research.
Technical Contribution
Introduced the Overediting Matrix to quantify models' overediting behavior. The Face-Edit-Attributes dataset offers a more detailed attribute classification for facial editing.
Novelty
First large-scale evaluation of text-guided models for facial editing, offering a comprehensive attribute classification and bias analysis compared to prior works.
Limitations
- Limitation 1: Models struggled with pose edits, possibly due to insufficient training data.
- Limitation 2: Overediting is prevalent, affecting edit precision.
Future Work
Future work could explore methods to reduce overediting and further study fairness across demographic features.
AI Executive Summary
Facial editing is a crucial task in computer vision, widely used in popular applications like FaceApp and Photoshop. Traditionally, Generative Adversarial Networks (GANs) and 3D Morphable Models (3DMMs) have been employed for facial editing, each with its pros and cons. GANs can perform diverse facial edits but often produce unstable results, while 3DMMs can stably edit pose and expression. Recently, text-guided diffusion models like Nano Banana have gained popularity for image editing, offering a compelling alternative as they can produce both stable and varied edits.
This study presents the first large-scale evaluation of six popular text-guided models for facial editing tasks. We introduce Face-Edit-Attributes, the largest collection of facial editing attributes focused on hair, accessories, and pose edits. Using two popular celebrity face datasets, CelebA and CelebSET, we compared model performance. Our results show that most models performed well on hair and accessory edits but struggled with pose edits. Additionally, all models exhibited overediting, particularly for dark-skinned male and old faces.
The study reveals potential bias issues in text-guided models for facial editing, providing crucial insights for future fairness research. We propose a new evaluation metric, the Overediting Matrix, to quantify models' overediting behavior. Future research could explore methods to reduce overediting and further study fairness across demographic features.
Deep Analysis
Background
Facial editing is an important research direction in computer vision, widely applied in virtual try-ons and content creation. Traditionally, Generative Adversarial Networks (GANs) and 3D Morphable Models (3DMMs) have been used for facial editing. GANs can perform diverse facial edits but often produce unstable results, while 3DMMs can stably edit pose and expression. Recently, text-guided diffusion models like Nano Banana have gained popularity for image editing, offering a compelling alternative as they can produce both stable and varied edits.
Core Problem
Although text-guided models perform well in scene editing, their performance in facial editing has not been comprehensively evaluated. Facial editing requires models to perform fine-grained edits while maintaining facial identity integrity. Additionally, fairness across demographic features is an important issue.
Innovation
This study presents the first large-scale evaluation of text-guided models for facial editing. We introduce Face-Edit-Attributes, the largest collection of facial editing attributes focused on hair, accessories, and pose edits. Additionally, we propose a new evaluation metric, the Overediting Matrix, to quantify models' overediting behavior.
Methodology
- �� Evaluate six text-guided models for facial editing, including Nano Banana and Flux-Dev.
- �� Use the Face-Edit-Attributes dataset, covering 169 facial editing attributes.
- �� Assess models using CelebA and CelebSET datasets, focusing on hair, accessory, and pose edits.
- �� Evaluate models' overediting behavior and demographic biases.
Experiments
Experiments use CelebA and CelebSET, two popular celebrity face datasets, for evaluation. We created four test sets: Hair-Test, Accessories-Test, Pose-Test, and Multi-Axis-Test. Each test set includes multiple edit sessions, each with four editing instructions. We use three evaluation metrics: Facial Similarity Score (FSS), Semantic Consistency (SC), and Overediting Matrix.
Results
Results show that most models performed well on hair and accessory edits but struggled with pose edits. Additionally, all models exhibited overediting, particularly for dark-skinned male and old faces. Qwen-Edit and Flux-Dev showed consistent gender and skin-tone bias in identity preservation and edit execution.
Applications
Applications of text-guided models in facial editing include virtual try-ons, content creation, and personalized advertising. Models can perform fine-grained facial edits according to user needs, such as changing hairstyles and adding accessories.
Limitations & Outlook
Models struggled with pose edits, possibly due to insufficient training data. Additionally, overediting is prevalent, affecting edit precision. Future research could explore methods to reduce overediting and further study fairness across demographic features.
Plain Language Accessible to non-experts
Imagine you're in a hair salon where a stylist can change your hairstyle, add accessories, or adjust your pose based on your instructions. This stylist is like a text-guided model that performs facial edits based on your commands. You tell them you want a new hairstyle, and they not only change your hairstyle but might also change your hair color, which is called overediting. This stylist might exhibit bias when handling customers of different skin tones and ages, such as performing more overedits for dark-skinned male and older customers. Our study evaluates this stylist's performance to see where they excel and where they need improvement.
ELI14 Explained like you're 14
Imagine you're playing a game where you can change your character's hairstyle, put on cool glasses, or make them look in different directions. The character editing in this game is like the text-guided models we studied. We found these models are great at changing hairstyles and adding glasses but a bit clumsy at changing poses. Plus, they sometimes go overboard, like changing hair color when you only wanted a new hairstyle. What's more interesting is these models behave differently with characters of different skin tones and ages, making more mistakes with dark-skinned and older characters. Our study is all about finding these issues and figuring out how to fix them.
Glossary
Text-guided Models
Models that perform image editing based on text instructions, capable of executing diverse editing tasks.
Used for facial editing tasks, executing hair, accessory, and pose edits.
Generative Adversarial Networks
A type of generative model that uses two networks competing against each other to create realistic images.
Used for facial editing but often produces unstable results.
3D Morphable Models
Models based on 3D facial renderings, capable of stably editing pose and expression.
Used for facial editing but can only edit pose and expression.
Facial Similarity Score
A score used to evaluate the similarity of the edited image to the original image's facial identity.
Used to assess models' ability to maintain facial identity during editing.
Overediting Matrix
A metric used to quantify the additional edits performed by models beyond the intended edit.
Used to evaluate models' overediting behavior.
Open Questions Unanswered questions from this research
- 1 How to reduce models' overediting to improve edit precision and consistency.
- 2 How to improve models' fairness across demographic features to reduce bias.
Applications
Immediate Applications
Virtual Try-ons
Users can change hairstyles and accessories through text instructions to experience different looks.
Personalized Advertising
Advertisers can customize ad content based on users' facial features.
Long-term Vision
Fairness Research
Explore methods to reduce model bias, ensuring fairness in facial editing across demographic features.
Abstract
Facial appearance editing powers popular applications like FaceApp and Photoshop. Generative Adversarial Networks (GANs) and 3D Morphable Models (3DMMs) have been widely used for facial editing. GANs can perform varied facial edits (e.g., changing hair color, hairstyle), but often produce unstable edits. 3DMMs produce stable edits, but can only alter pose and facial expression. Recently, text-guided diffusion models like Nano Banana have become popular for image editing. Text-guided models are a compelling alternative to GANs and 3DMMs since they can produce both stable and varied image edits. While text-guided models have been widely tested for whole-scene edits (e.g., ``make the woman play a guitar''), they have not been comprehensively tested for facial editing. We conducted the first large-scale evaluation ($\sim1$M images evaluated) of six popular text-guided models on a sequential facial editing task. We present Face-Edit-Attributes, the largest collection of $169$ facial editing attributes focused on hair, accessories, and pose edits. We compared model performance using two popular celebrity face datasets: CelebA and CelebSET. Our results show that most models performed hair and accessory edits well, but struggled with editing pose. All models over-edit (e.g., changing hair color when asked only to change the hairstyle). We also evaluated demographic biases in each model. Our results show surprising biases in overediting: almost all models created more overedits for dark-skinned male faces and old faces. The code and data for our results (including our repository of $\sim 1$M images) can be accessed \href{https://github.com/rahul1801/Face-Edit-Bench}{\textcolor{blue}{here}}.