A Large-scale Evaluation of Text-guided Models for Facial Editing
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The paper introduces Multi-Tool Image Editing Attribution (MIEA), a task that identifies multiple generative AI tools used to edit facial images. It presents a new dataset, MultiEdit, with over 500,000 edited faces covering six editing tool types, including face swapping and facial enhancements. The authors propose DPEC, a method that captures tool-specific traces in spatial and frequency domains using an error-based curriculum learning strategy, outperforming nine baseline methods on images edited in up to five steps.
Recent advances in unified multimodal models have significantly improved text-guided image editing abilities. In particular, models such as Nano-Banana-Pro and GPT-Image-2 demonstrate emerging capabilities in multi-source image editing (MIE), including tasks such as object synthesis, person-background composition, and cross-image style fusion.
arXiv:2606. 01213v1 Announce Type: cross Abstract: Despite tremendous recent progress, current text-guided image editing methods still struggle with many aspects of editing involving instruction following, minimally editing the source image, and ensuring high visual quality.
Generating and editing a person's face demands high precision, as even minor modifications can significantly alter a subject's perceived identity. Current personalization and editing methods built on general-purpose text-to-image models, however, often lack the precision required for fine-grained facial edits.
arXiv:2609.01409v1 Announce Type: new Abstract: Vision-language models (VLMs) have shown strong performance in generating scientific figures from text or images. However, producing publication-ready...
arXiv:2606. 08016v1 Announce Type: cross Abstract: Current image editing software often hinges on fixed filters or expert tuning, leaving a gap between amateur users' intent and outcomes.