arXiv:2605. 09233v2 Announce Type: replace-cross Abstract: Recent advances in visual generative models have enabled high-fidelity image editing guided by human instructions.
arXiv:2510. 08532v2 Announce Type: replace-cross Abstract: Instruction-based image editing offers a powerful and intuitive way to manipulate images through natural language.
arXiv:2606. 05950v1 Announce Type: new Abstract: Text-guided image editing has advanced rapidly with diffusion models and unified multimodal foundation models.
arXiv:2509. 24900v2 Announce Type: replace-cross Abstract: The performance of unified multimodal models for image generation and editing is fundamentally constrained by the quality and comprehensiveness of their training data.
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.
Existing instruction-based video editing datasets commonly focus on single-task appearance editing, failing to meet the complex creative demands of real-world scenarios. To bridge this gap, we present Goku, a large-scale dataset featuring 2 million high-quality, instruction-aligned video editing pairs, which is the first to extend task boundaries from basic appearance editing to multi-task and structural manipulations(e.
arXiv:2606. 00188v1 Announce Type: cross Abstract: While current multimodal models are proficient at open-ended visual editing, executing precise single-answer edits remains an important obstacle.
Recent breakthroughs in instruction-based image editing have captured significant attention, as models are now capable of handling real-world editing demands with the practicality required by everyday users. However, editing models trained primarily for single-turn edits often break down in multi-turn editing--the natural interactive setting where a user iteratively refines an image based on the model's own previous outputs.
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.
arXiv:2606. 08415v1 Announce Type: cross Abstract: While recent text-guided video editing models excel at elementary tasks (e.
arXiv:2606. 11751v1 Announce Type: cross Abstract: Multi-turn image editing is essential for iterative design, yet current models often struggle with identity drift and error accumulation over successive steps.
Despite progress in instruction-based video editing, unimodal textual instructions inherently struggle to convey fine-grained textures and complex dynamics. To bridge this perceptual gap, we propose Visual In-context Editing, a new paradigm elevating video editing from textual instructions to multi-modal visual guidance encompassing single image, image pair, and video pair.
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.