arXiv Computer Vision
Oct 9

Rubric-CEPR: Self-Evolving Image Editing via Reward-Verified Self-Distillation

Rubric-CEPR is a self‑evolving image editing framework that improves a pretrained editor using only its own generated samples, without human‑edited targets or external reward models. It employs a planner to generate structured edit instructions, an editor to produce multiple candidate edits, and a frozen critic that scores each candidate with rubric‑augmented contrastive edit‑preservation rewards, rejecting infeasible candidates via non‑compensatory gates. The best verified candidate is distilled into the editor through lightweight adapter training, yielding measurable performance gains on benchmarks such as Qwen‑Image‑Edit, GEdit‑Bench, and Complex‑Edit.

By Ritesh Thawkar, Shubham Patle, Shravan Venkatraman, Rao Muhammad Anwer
Hugging Face Trending Papers
Oct 5

Scalable Minimal-Change Learning for Controllable Image Editing

The paper introduces ARRO, a reinforcement‑learning framework that enforces minimal‑change editing by auditing source images, instructions, and edited outputs for unimplemented and unintended changes. Using a vision‑language reward model and a group‑level rubric, ARRO achieves higher EditScore on multiple benchmarks and reduces off‑target pixel changes by 8.4% on FLUX.1 Kontext‑dev. The approach eliminates the need for per‑instruction human annotations and demonstrates strong performance across several evaluation sets.

arXiv Computer Vision
Sep 18

Refinement Is Inherently Editable: Training-Free Prompt-to-Prompt Image Editing with Generative Refinement Network

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
arXiv Computer Vision
Sep 14

Semantically Aligned Gradient-Driven Context-Preserving Image Editing

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 AI
Oct 9

VINCIE-NExT: Unlocking Video Editing from Images via In-Context Modeling

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