arXiv:2605. 09233v2 Announce Type: replace-cross Abstract: Recent advances in visual generative models have enabled high-fidelity image editing guided by human instructions.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
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.