Large-scale text-to-image models are attractive backbones for dense prediction because RGB generation pretraining learns rich semantic, structural, and geometric priors. Existing generative and editing approaches reuse these priors by casting dense prediction as target generation: annotations such as depth, normals, alpha mattes, masks, and heatmaps are encoded into an RGB-trained VAE latent space and decoded back as image-like targets.
PredErase is a training‑free method that removes objects and their photometric effects by guiding a frozen Fill model with predictive latent cues. It expands the user‑provided mask to a contact‑band region, uses I‑JEPA to generate a context‑conditioned target for the hole, and aligns Fill’s completions with this target while keeping surrounding pixels fixed. On benchmarks such as RemovalBench, RORD‑Val, and DEFACTO‑Val, PredErase improves the native FLUX.2 backbone for instance‑only masks, though supervised removers still outperform it on full‑image metrics.
By Waikit Xiu, Qiang Lu, Junbiao Chen, Xiying Li
arXiv:2607.18227v2 Announce Type: replace
Abstract: In line with the prevailing direction of vision research, we explore the integration of both generation and editing capabilities for video and imag...
By Dingyun Zhang, Lixue Gong, Wei Liu
RefineEdit is a training‑free prompt‑to‑prompt image editing framework that uses a Generative Refinement Network to edit images by refining binary image codes. It couples edit localization with content generation, selecting editable positions based on signed probability differences between an editing branch and a source branch, and stabilizes edits with adaptive spatial freezing and finite bit locking. The method requires no additional training, external masks, or attention control, and outperforms other methods on PIE‑Bench in background‑preservation metrics and CLIP scores.
By Yulong Chen, Ziqian Zhang, Haoyu Zhang, Ao He, Senmao Li, Kai Wang
Subject-driven image personalization---generating new images that preserve the identity of one or several reference subjects in novel scenes---is a foundational capability for modern visual content creation. It is currently dominated by generalized methods that fine-tune a pretrained multimodal diffusion transformer (MMDiT) on hundreds of thousands to millions of paired \emph{(reference, composed-target)} examples, where each composed target is a synthesized image of the subject in a novel scene.
Semantically Aligned Gradient-Driven Context-Preserving Image Editing (IABEdit) is a model‑agnostic framework that embeds differentiable semantic verification into the training of generative image editors. By using a frozen vision‑language model to extract spatially‑aware descriptors from ground‑truth edits and a trainable aligner to reproduce them from generated outputs, the residual becomes a gradient that teaches the generator both what to edit and where, without adding inference‑time VLM cost. IABEdit is compatible with various backbones (e.g., U‑Net in Stable Diffusion and MMDiT in FLUX) and improves structural fidelity on MagicBrush, achieves state‑of‑the‑art instruction adherence on RealEdit and EMU Edit, and outperforms the proprietary Gemini agent on the D‑LORD surveillance benchmark under heavy occlusion.
"whyItMatters":"IABEdit demonstrates that incorporating semantic verification during training can produce more accurate, well‑localized edits and outperform existing methods even in challenging surveillance scenarios, as shown by its superior metrics and human/GPT‑4o evaluations."
By Chiranjeev Chiranjeev, Muskan Dosi, Mayank Vatsa, Richa Singh
arXiv:2610.27592v1 Announce Type: new
Abstract: Aiming to modify textual content while preserving the original style, Scene Text Editing (STE) has significant practical value in many applications. ST...
By Xingsong Ye, Yongkun Du, Jiaxin Zhang, Chong Sun, Zhineng Chen
VINCIE‑NExT is a unified framework that transfers image editing capabilities to video editing by using in‑context visual demonstrations. It decomposes video editing into a chain of sub‑tasks (Video → Image → Image → Video), routing editing intent through the image domain and enabling joint training from heterogeneous image and video data under a single diffusion objective. The method introduces a novel position encoding to link image demonstrations and video frames, allowing pixel‑faithful appearance propagation, and supports test‑time scaling through progressive diffusion stages.
By Leigang Qu, Feng Cheng, Ziyan Yang, Bangbang Yang, Zhaoyang Huang, Wei Chow, Yicong Li, Wenjie Wang, Tat-Seng Chua, Yan Zeng
arXiv:2607. 21318v1 Announce Type: cross Abstract: Replacing an object with one that differs in category or shape requires complete source removal, natural target formation unconstrained by the source silhouette, and preservation of unrelated content.
By Jian Zhang, Zhijun Zhang
arXiv:2606. 13898v1 Announce Type: cross Abstract: Creative image editing tools, such as Photoshop's Remove or Generative Fill buttons, are central to everyday customer use and account for a major share of traffic in Photoshop and Lightroom.
By Haoran You, Yotam Nitzan, Lingzhi Zhang, Yifan Gong, Mang-Tik Chiu, Connelly Barnes, Yan Kang, Yuqian Zhou, Eli Shechtman, Sohrab Amirghodsi
arXiv:2608.20929v1 Announce Type: new
Abstract: AI-generated image manipulation localization identifies edited pixels, but its OOD performance lags behind image-level detection partly because pixel s...
By Haozhen Yan, Siyuan Shan, Zijian Yu, Youqi Wang, Yan Hong, Jun Lan, Jianfu Zhang
DuplexBridge is a unified image‑editing model that coordinates its own understanding and generation capabilities without relying on an external module. It uses a source‑grounded bridge to specify editing targets, final‑state changes, and preservation constraints, and employs reciprocal trust checks to selectively share global task feedback while maintaining independent local supervision. The system, evaluated on the new CE‑Bench and other benchmarks, achieves 21.75–25.75 percentage points higher complete compliance than direct editors and demonstrates the effectiveness of distinct local responsibilities with selective shared feedback.
By Yidi Liu, Jie Xiao, Chaojie Mao, Jingfeng Zhang, Xueyang Fu