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.
By Sheng Liu, Qiang Sheng, Danding Wang, Yu Li, Chenming Zhou, Juan Cao
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.
By Aishwarya Agrawal, Roy Hirsch, Yasumasa Onoe, Sherry Ben, Jason Baldridge
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...
By Christian Greisinger, Zhixue Zhao, Steffen Eger
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.
By Zichen Zhu, Yuheng Sun, Mingxuan Zhu, Wenjie Ma, Situo Zhang, Zhexiang Wang, Ziyue Yang, Danyang Zhang, Kunyao Lan, Zihan Zhao, Dingye Liu, Siqi Xiang, Lu Chen, Kai Yu
The paper introduces COSY, a new generator architecture for 3D Gaussian Splatting (3DGS) GANs that synthesizes human head components—hair, skin, glasses, and torso—independently, enabling precise editing of individual attributes without affecting others. It achieves this disentanglement using only sparse color cues, avoiding the need for segmentation masks or geometric priors, and incorporates minimal shared context tokens to maintain consistent shape and lighting. Compared to prior methods, COSY offers improved disentanglement, more accurate editing control, and competitive visual quality.
By Florian Barthel, Shalini De Mello, Koki Nagano, Wieland Morgenstern, Anna Hilsmann, Peter Eisert
VibeEdit is a new image‑editing tool that lets users draw spatial marks and add brief notes directly on an image to create a canvas instruction. These instructions guide the model to perform tasks such as adding, removing, replacing, or moving objects, as well as modifying attributes, without needing a separate text prompt. The system is trained on 1.55 million annotated edit pairs and uses a layer‑decoupled conditioning approach, achieving higher VLM rubric scores and PSNR on a benchmark that tests target selection among similar objects.
By Jinjing Zhao, Fangyun Wei, Yitong Wang, Xiuyu Wu, Yunuo Chen, Yang Yue, Sirui Zhang, Wenbo Wang, Hongyang Zhang, Dong Chen, Yan Lu, Chang Xu
arXiv:2610.01670v1 Announce Type: new
Abstract: Multimodal large language models (MLLMs) are increasingly used as automated judges for instruction-based image editing and as reward signals for model...
By Yuan Huang, Zirui Song, Xiuying Chen
arXiv:2607. 05310v1 Announce Type: new Abstract: Model editing promises a fast, targeted way to correct post-deployment mistakes in medical vision-language models (VLMs) without costly retraining.
By Guli Zhu, Chenwei Wu, Liyue Shen
The demand for image manipulation has seen a significant increase recently. Traditional tools like Photoshop and Capture One, while powerful, require considerable expertise to use effectively.
AVENUE is a new benchmark and evaluation framework for audio‑video editing that includes 1,291 source clips and 7,957 editing instructions covering audio‑targeted, video‑targeted, and coupled edits. It introduces a sample‑specific, modality‑aware evaluation that specifies the intended change and the content that must remain unchanged. The study applies this framework to joint, sequential, and separate editing models, revealing that existing models often alter unintended modalities, highlighting a key challenge in controllable AV editing.
By Hayeon Kim, Yoojin Jang, Jaejun Yoo