FRPSS：单图像生成中的预形状空间特征重排
FRPSS: Feature Rearrangement in Pre-Shape Space for Single-Image Generation
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中文总结 AI 辅助
针对单图像生成中结构错位问题，提出FRPSS方法，通过预形状空间特征重排与测地线表面特征增强，在三个数据集上取得最优SIFID并支持下游任务。
中文摘要 AI 辅助
在单张图像上训练的生成模型往往难以平衡全局结构完整性与局部多样性。现有的单图像生成方法通常依赖随机噪声驱动生成过程，缺乏明确的全局结构约束，导致在结构变化时生成结果容易出现空间结构错位。为解决这一问题，本文提出了单图像生成中的预形状空间特征重排（FRPSS）。FRPSS的核心是测地线表面特征增强的流形结构重排（MSR-FAGS）模块。MSR-FAGS将低尺度生成器的随机初始化特征替换为重排后的预形状特征，并利用这些特征指导后续尺度的图像生成，从而降低结构错位的风险。为支持风格化等下游任务，进一步设计了尺度自适应滑动窗口补丁提取（SSPE）策略，并构建了带有SSPE的定向对比语言-图像预训练监督模块（CLIP-SSPE）。定性和定量实验表明，FRPSS在全部三个数据集上取得了最佳的单图像Fréchet Inception Distance（SIFID）分数，同时保持了具有竞争力的学习感知图像补丁相似度（LPIPS）。进一步的定性实验验证了FRPSS在多个下游任务中结合CLIP-SSPE模块的有效性。
英文摘要
Generative models trained on a single image often struggle to balance global structural integrity and local diversity. Existing single-image generation methods commonly rely on random noise to drive the generation process and lack explicit global structural constraints, making the generated results prone to spatial structural misalignment when structural variations occur. To address the issue, Feature Rearrangement in Pre-Shape Space for Single-Image Generation (FRPSS) is proposed in this paper. The core of FRPSS is the Manifold Structural Rearrangement with Feature Augmentation on Geodesic Surface (MSR-FAGS) module. MSR-FAGS replaces the randomly initialized features of the low-scale generator with rearranged Pre-Shape features and uses the features to guide image generation at subsequent scales, thereby reducing the risk of structural misalignment. To support downstream tasks such as stylization, a Scale-adaptive Sliding-window Patch Extraction (SSPE) strategy is further designed, and a directional Contrastive Language-Image Pre-training supervision module with SSPE (CLIP-SSPE) is constructed. Qualitative and quantitative experiments demonstrate that FRPSS achieves the best Single Image Fréchet Inception Distance (SIFID) scores on all three datasets while maintaining competitive Learned Perceptual Image Patch Similarity (LPIPS). Further qualitative experiments verify the effectiveness of FRPSS across multiple downstream tasks with the CLIP-SSPE module.
发表机构
- Shanghai University（上海大学）
机构由 AI 辅助整理，请以论文原文为准。
