StructDiff: A Structure-Preserving and Spatially Controllable Diffusion Model for Single-Image Generation
This paper introduces StructDiff, a generative framework based on a single-scale diffusion model for single-image generation. Single-image generation aims to synthesize diverse samples with similar visual content to the source image by capturing its internal statistics, without relying on external data. However, existing methods often struggle to preserve the structural layout, especially for images with large rigid objects or strict spatial constraints. Moreover, most approaches lack spatial controllability, making it difficult to guide the structure or placement of generated content. To address these challenges, StructDiff introduces an \textit{adaptive receptive field} module to mainta...
| Authors | Yinxi He, Kang Liao, Chunyu Lin et al. |
|---|---|
| Primary category | cs.CV |
| Published | 14 April 2026 |
| Last updated at source | 14 April 2026 |
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