AI summaryⓘ
The authors introduce a new image editing approach called TV-Edit that combines text instructions (which explain what to do) with simple visual hints like dragging or pointing (which show where to do it). They created a big dataset of paired text and visual instructions from videos to help train their system to understand both kinds simultaneously. Their method improves how accurately the edits match the intended meaning and location compared to using only text or only visual hints. They also made a test set, TV-Edit-Bench, to better evaluate how well the edits follow instructions and stay consistent. Overall, their experiments show TV-Edit works better than previous editing tools that rely just on text or visual prompts.
image editingtextual instructionsvisual promptssemantic intentspatial guidancecross-modal instructionTV-Editbenchmarkstructural consistencysemantic faithfulness
Authors
Chenxi Xie, Yuhui Wu, Qiaosi Yi, Lei Zhang
Abstract
Existing image editing methods can be generally categorized into textual instruction-based and visual prompt-based ones. Textual instructions are semantically expressive, but are limited by the coarse granularity of spatial control of the editing results. In contrast, visual prompts such as drag and point can provide precise spatial guidance, but are limited by the inherent ambiguity in semantic intent. To unify the strength of textual and visual prompts, we present Text-Vision Co-Instructed Image Editing, which jointly models textual instructions as semantic intent and sparse visual instructions as spatial guidance, aiming to achieve precise and intent-faithful image manipulation. To this end, we first construct a textual-visual instruction paired dataset with more than 23K samples derived from dynamic videos, enabling aligned supervision for cross-modal instruction. We then propose TV-Edit, a Textual-Visual instruction unified Editing framework to contextualize drag or point-based visual instructions with image-text semantics and lift them into semantic-aware control representations for pretrained editing backbones. By integrating semantic intent and spatial constraints, TV-Edit leads to more precise spatial control, less instruction ambiguity, and stronger structural consistency than text-only or drag-based alternatives. Finally, we establish TV-Edit-Bench, a deliberately designed benchmark to evaluate semantic faithfulness, spatial alignment, and visual consistency with ground-truth references and controlled textual-visual variations for reliable assessment. Our experiments across multiple editing backbones demonstrate that TV-Edit consistently yields more precise and intent-faithful edits, significantly outperforming state-of-the-art instruction-based and drag-based baselines.