Envisioning Beyond the Few: Disentangled Semantics and Primitives for Few-Shot Atypical Layout-to-Image Generation
arXiv:2605.31266v1 Announce Type: cross Abstract: The layout-to-image (L2I) task enables fine-grained control over image generation via object categories and spatial layouts. However, existing L2I methods yield fragmented and distorted generations under few-shot atypical settings. We term this failure as representation fragmentation, arising from a granularity mismatch that entangles semantic identity with visual details. To address this issue, we propose a representation-driven framework that disentangles semantics from primitives for robust few-shot adaptation. Specifically, Semantic Anchori
The paper addresses current limitations in few-shot atypical layout-to-image generation, a critical area for more robust and versatile AI image synthesis, indicating ongoing advancements in generative AI capabilities.
Improved few-shot atypical L2I generation significantly enhances the ability to create customizable, high-quality images from limited data, impacting various applications from design to simulated environments.
The ability to disentangle semantics from primitives will allow for more stable and accurate image generation, reducing 'representation fragmentation' in challenging, data-scarce scenarios.
- · Generative AI researchers
- · Creative industries
- · AI-powered design platforms
- · Digital content creation
- · Traditional image generation methods
- · Models reliant on large-scale datasets for specific tasks
More accurate and controllable AI image generation with fewer examples.
Accelerated development of AI tools for rapid prototyping and bespoke content creation across industries.
Potentially democratizes high-quality image generation, reducing barriers for small creators and businesses.
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Read at arXiv cs.LG