Duplicate from PixelSmile/FFE-Bench

Co-authored-by: Jiabin Hua <Ammmob@users.noreply.huggingface.co>

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README.md

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+---
+language:
+- en
+license: apache-2.0
+task_categories:
+- image-to-image
+---
+
+<h2 align="center" style="margin-bottom: 0px;">
+  <span style="display: inline-flex; align-items: center; gap: 8px;">
+    <img src="./assets/PixelSmile.png" width="25">
+    <span>FFE-Bench: A Benchmark for Fine-Grained Facial Expression Editing</span>
+  </span>
+</h2>
+
+<div align="center">
+  <a href="https://arxiv.org/abs/2603.25728"><img src="https://img.shields.io/badge/arXiv-Paper-b31b1b.svg" alt="Paper"></a> &nbsp;&nbsp;&nbsp;&nbsp;
+  <a href="https://ammmob.github.io/PixelSmile/"><img src="https://img.shields.io/badge/Project-Page-Green" alt="Project Page"></a> &nbsp;&nbsp;&nbsp;&nbsp;
+  <a href="https://github.com/Ammmob/PixelSmile"><img src="https://img.shields.io/badge/GitHub-Code-blue" alt="GitHub Code"></a> &nbsp;&nbsp;&nbsp;&nbsp;
+  <a href="https://huggingface.co/PixelSmile/PixelSmile"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-orange" alt="Model"></a> &nbsp;&nbsp;&nbsp;&nbsp;
+  <a href="https://huggingface.co/spaces/PixelSmile/PixelSmile-Demo"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Demo-8b5cf6" alt="Demo"></a>
+</div>
+
+<br>
+
+FFE-Bench was introduced in the paper [PixelSmile: Toward Fine-Grained Facial Expression Editing](https://huggingface.co/papers/2603.25728).
+
+## 📘 Dataset Overview
+
+FFE-Bench is a benchmark for fine-grained facial expression editing across both human and anime portraits, with richer and more diverse expression categories designed to evaluate controllable facial editing in realistic settings.
+
+The current release contains 198 editing tasks in total, including 98 human samples and 100 anime samples. Each sample is defined by an input image, a face bounding box, a target expression category, and a text prompt without intensity modifiers.
+
+## 📏 Evaluation Metrics
+
+The metric definitions follow the [paper](https://huggingface.co/papers/2603.25728).
+
+- `Mean Structural Confusion Rate (mSCR)`: evaluates structural confusion between semantically overlapping expressions.
+- `Accuracy (Acc)`: evaluates expression editing accuracy.
+- `Control Linearity Score (CLS)`: evaluates linear controllability.
+- `Harmonic Editing Score (HES)`: evaluates the overall balance between expression editing quality and identity preservation.
+- `Identity Similarity (ID Sim)`: evaluates identity consistency between the source and edited faces.
+
+## 📈 Benchmark Results
+
+We report two complementary benchmark settings:
+
+- `General Editing`: compares general-purpose editing models and facial editing models on structural confusion, editing accuracy, and identity consistency.
+- `Linear Control`: compares methods designed for controllable expression manipulation on control linearity, editing quality, and identity preservation.
+
+The current benchmark results include the following models:
+
+- `General Editing`: Seedream-4.5, Nano Banana Pro, GPT-Image-1.5, FLUX.2 Klein, LongCat-Image-Edit, Qwen-Image-Edit-2511, and PixelSmile.
+- `Linear Control`: SAEdit, ConceptSlider, AttributeControl, Kontinuous-Kontext, SliderEdit, and PixelSmile.
+
+<p align="center">
+  <img src="./assets/ffe-bench-general.png" alt="FFE-Bench General Editing Results" width="80%">
+</p>
+
+<p align="center">
+  <img src="./assets/ffe-bench-linear.png" alt="FFE-Bench Linear Control Results" width="80%">
+</p>
+
+## 📊 Evaluation Code
+
+The evaluation code will be released soon at the [GitHub repository](https://github.com/Ammmob/PixelSmile).
+
+## 📖 Citation
+
+If you find FFE-Bench useful in your research or applications, please consider citing our work.
+
+```bibtex
+@article{hua2026pixelsmile,
+  title={PixelSmile: Toward Fine-Grained Facial Expression Editing},
+  author={Jiabin Hua and Hengyuan Xu and Aojie Li and Wei Cheng and Gang Yu and Xingjun Ma and Yu-Gang Jiang},
+  journal={arXiv preprint arXiv:2603.25728},
+  year={2026}
+}
+```

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