arXiv:2602.15727v2 Announce Type: replace-cross Abstract: Visual analogy learning enables image editing via demonstration rather than textual description, allowing users to specify complex transformations difficult to articulate in words. Given a triplet $\{\mathbf{a}$, $\mathbf{a}'$, $\mathbf{b}\}$, the goal is to generate $\mathbf{b}'$ such that $\mathbf{a} : \mathbf{a}' :: \mathbf{b} : \mathbf{b}'$. Recent methods adapt text-to-image models with a single Low-Rank Adaptation (LoRA) module, but they face a fundamental limitation: attempting to capture the diverse space of visual transformatio
This research is emerging now due to rapid advancements in AI models and the increasing demand for intuitive, non-textual interaction methods in image generation and editing.
This work represents a step forward in visual AI by making image generation and manipulation more accessible and powerful for users without requiring complex textual prompts.
The ability to represent visual transformations more broadly and precisely through a LoRA weight basis enables more sophisticated and controllable image editing via analogy.
- · AI researchers
- · Generative AI platforms
- · Digital artists
- · Creative industries
- · Platforms reliant solely on text-to-image prompts
- · Legacy image editing software
More accurate and creative visual analogy generation in AI systems.
Accelerated development of visual AI tools that understand and apply complex stylistic and structural transformations.
Enhanced automation of visual content creation, impacting graphic design, marketing, and media production workflows.
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Read at arXiv cs.LG