FED-Bench: A Cross-Granular Benchmark for Disentangled Evaluation of Facial Expression Editing

• URL: http://arxiv.org/abs/2603.29697v1
• Date: Tue, 31 Mar 2026 12:52:17 GMT
• Title: FED-Bench: A Cross-Granular Benchmark for Disentangled Evaluation of Facial Expression Editing
• Abstract summary: FED-Bench is a comprehensive benchmark featuring rigorous testing and an accurate evaluation suite.<n>We benchmark 18 image editing models, revealing that current approaches struggle to simultaneously achieve high fidelity and accurate expression manipulation.<n>Our benchmark and related code will be made publicly open soon.
• Score: 29.7144418122336
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Facial expression image editing requires fine-grained control to strictly preserve human identity and background while precisely manipulating expression. However, existing editing benchmarks primarily focus on general scenarios, lacking high-quality facial images and corresponding editing instructions. Furthermore, current evaluation metrics exhibit systemic biases in this task, often favoring lazy editing or overfit editing. To bridge these gaps, we propose FED-Bench, a comprehensive benchmark featuring rigorous testing and an accurate evaluation suite. First, we carefully construct a benchmark of 747 triplets through a cascaded and scalable pipeline, each comprising an original image, an editing instruction, and a ground-truth image for precise evaluation. Second, we introduce FED-Score, a cross-granularity evaluation protocol that disentangles assessment into three dimensions: Alignment for verifying instruction following, Fidelity for testing image quality and identity preservation, and Relative Expression Gain for quantifying the magnitude of expression changes, effectively mitigating the aforementioned evaluation biases. Third, we benchmark 18 image editing models, revealing that current approaches struggle to simultaneously achieve high fidelity and accurate expression manipulation, with fine-grained instruction following identified as the primary bottleneck. Finally, leveraging the scalable characteristic of introduced benchmark engine, we provide a 20k+ in-the-wild facial training set and demonstrate its effectiveness by fine-tuning a baseline model that achieves significant performance gains. Our benchmark and related code will be made publicly open soon.

Related papers

• PaintBench: Deterministic Evaluation of Precise Visual Editing [50.50559031686293]
  PaintBench is a benchmark for 20 fundamental precise visual editing operations.<n>Across 11 image editing models, we find overall low performance, with the current highest-performing industry leader scoring only 17.1%.<n>Fine-grained benchmark diagnostics show performance degradations induced by scene variations in object count, background complexity, color scheme, and edit-region size.
  arXiv  Detail & Related papers  (2026-05-29T16:01:14Z)
• ReasonEdit: Towards Interpretable Image Editing Evaluation via Reinforcement Learning [86.61218827780675]
  We present ReasonEdit, an evaluation tool for text-guided image editing.<n>It is trained using reward signals derived from RE-Reward and the Group Relative Policy Optimization (GRPO) algorithm.<n>It is capable of generating high-quality interpretable evaluation text, enabling more transparent and trustworthy assessment for image editing.
  arXiv  Detail & Related papers  (2026-05-08T09:23:26Z)
• StableI2I: Spotting Unintended Changes in Image-to-Image Transition [35.72064037549274]
  We propose StableI2I, a unified and dynamic evaluation framework that measures content fidelity and pre-post consistency.<n>Our framework serves as a practical and reliable evaluation tool for diagnosing content consistency and benchmarking model performance in real-world I2I systems.
  arXiv  Detail & Related papers  (2026-05-06T03:28:05Z)
• GEditBench v2: A Human-Aligned Benchmark for General Image Editing [58.86807672117726]
  We introduce GEditBench v2, a comprehensive benchmark with 1,200 real-world user queries spanning 23 tasks.<n>We also propose PVC-Judge, an open-source pairwise assessment model for visual consistency, trained via two novel region-decoupled preference data synthesis pipelines.<n> PVC-Judge achieves state-of-the-art evaluation performance among open-source models and even surpasses GPT-5.1 on average.
  arXiv  Detail & Related papers  (2026-03-30T15:08:32Z)
• Evaluating Image Editing with LLMs: A Comprehensive Benchmark and Intermediate-Layer Probing Approach [48.01137214342501]
  We introduce TIEdit, a benchmark for systematic evaluation of text-guided image editing methods.<n>To obtain reliable subjective ratings, 20 experts are recruited to produce 307,200 raw subjective ratings.<n>We further propose EditProbe, an LLM-based evaluator that estimates editing quality via intermediate-layer probing of hidden representations.
  arXiv  Detail & Related papers  (2026-03-20T09:05:10Z)
• RealDrag: The First Dragging Benchmark with Real Target Image [9.439854281295803]
  textbfRealDrag is the first comprehensive benchmark for point based image editing that includes paired ground truth target images.<n>Our dataset contains over 400 human annotated samples from diverse video sources.<n>We also propose four novel, task specific metrics: Semantical Distance (SeD), Outer Mask Preserving Score (OMPS), Inner Patch Preserving Score (IPPS), and Directional Similarity (DiS)
  arXiv  Detail & Related papers  (2025-12-13T11:14:03Z)
• GIE-Bench: Towards Grounded Evaluation for Text-Guided Image Editing [60.66800567924348]
  We introduce a new benchmark designed to evaluate text-guided image editing models.<n>The benchmark includes over 1000 high-quality editing examples across 20 diverse content categories.<n>We conduct a large-scale study comparing GPT-Image-1 against several state-of-the-art editing models.
  arXiv  Detail & Related papers  (2025-05-16T17:55:54Z)
• Inducing Predictive Uncertainty Estimation for Face Recognition [102.58180557181643]
  We propose a method for generating image quality training data automatically from'mated-pairs' of face images. We use the generated data to train a lightweight Predictive Confidence Network, termed as PCNet, for estimating the confidence score of a face image.
  arXiv  Detail & Related papers  (2020-09-01T17:52:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.