PreciseEdit is a new one‑step image editing framework that improves semantic transport by applying strong target‑directed changes only along reliable directions and spatial locations. It is model‑agnostic, training‑free, and inversion‑free, and uses a dual‑noise‑scale strategy to balance strong editing with background fidelity and structural consistency. Experiments on PIE‑Bench show that PreciseEdit outperforms existing methods across a range of editing tasks.
arXiv:2610.30827v1 Announce Type: new Abstract: Real-image editing commonly relies on inversion and iterative sampling, while efficient one-step editors often stabilize a large update by estimating a...
MTEdit is a new multi‑turn image editing dataset that uses packed trajectory canvas (PTC) generation to create 500K editing trajectories, each up to eight turns with an average of 5.38 turns. The dataset addresses visual‑quality drift by jointly generating multiple editing states within a unified visual context, covering both step‑wise and context‑aware editing scenarios. MTEdit-Bench evaluates models on instruction following, content consistency, visual‑quality drift resistance, and historical context usage, and fine‑tuned Qwen‑Image‑MTEdit outperforms other open‑source models on this benchmark.
arXiv:2605. 09233v2 Announce Type: replace-cross Abstract: Recent advances in visual generative models have enabled high-fidelity image editing guided by human instructions.
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.
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."
arXiv:2610.29248v1 Announce Type: new Abstract: One-step generative models enable fast text-guided image editing, yet they often introduce unintended changes to regions that should remain unchanged....
Recent diffusion editors perform diverse instruction-based edits while conditioning on the source image at every denoising step. Yet persistent source-image conditioning can limit how fully an edit is executed and how natural the result appears, especially when the target scene diverges substantially from the input.
ThinkDrag is a multimodal framework for drag-based image editing that incorporates visual reasoning to interpret sparse point constraints as meaningful object- and scene-level transformations. It can generate an explicit reasoning trace to clarify the intended edit, improving interpretability for ambiguous drags. The system introduces Drag Endpoint Markers for grounding drags in image space and is evaluated on DragBench++, a benchmark of challenging drag-based editing scenarios, achieving state‑of‑the‑art performance in generation quality and plausibility while maintaining competitive point-following precision.
Steering Fields introduce adaptive vector fields that re-estimate steering directions at each step of a flow-based text-to-image generation process, replacing the fixed global steering vectors traditionally used. By operating on noisy states, they provide a continuous trade-off between steering strength and content preservation, and allow simultaneous induction and inhibition of concepts without explicit spatial masks or object priors. The method achieves state-of-the-art safety steering benchmarks and can also function as a structure-preserving image-editing technique, delivering high semantic fidelity while remaining model-agnostic and inversion-free.
arXiv:2609.14899v1 Announce Type: new Abstract: Neural 3D scene editing is often evaluated by semantic alignment alone, although a convincing result may alter unrelated content or become inconsistent...
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.
arXiv:2606. 05950v1 Announce Type: new Abstract: Text-guided image editing has advanced rapidly with diffusion models and unified multimodal foundation models.