提升宠物图像生成的精准度，解决身份错乱问题。
Animalbooth: multimodal feature enhancement for animal subject personalization
- 用动物专用网络和自适应注意力模块增强特征对齐
- 通过频域滤波实现从整体结构到细节纹理的渐进生成
- 适合需要高保真动物图像生成的研究与应用
个性化动物图像生成因外观特征丰富和形态差异大而困难。现有方法常出现跨域特征错位，导致身份漂移。我们提出AnimalBooth框架，通过动物专用网络和自适应注意力模块强化身份保留，缓解跨域对齐误差。进一步引入频域控制的特征融合模块，在潜在空间中使用离散余弦变换滤波引导扩散过程，实现从全局结构到细节纹理的渐进生成。为推动该领域研究，我们构建了AnimalBench——一个高分辨率动物个性化数据集。大量实验表明，AnimalBooth在多个基准上持续优于强基线，显著提升身份保真度与感知质量。
原文摘要 · Abstract (English)
Personalized animal image generation is challenging due to rich appearance cues and large morphological variability. Existing approaches often exhibit feature misalignment across domains, which leads to identity drift. We present AnimalBooth, a framework that strengthens identity preservation with an Animal Net and an adaptive attention module, mitigating cross domain alignment errors. We further introduce a frequency controlled feature integration module that applies Discrete Cosine Transform filtering in the latent space to guide the diffusion process, enabling a coarse to fine progression from global structure to detailed texture. To advance research in this area, we curate AnimalBench, a high resolution dataset for animal personalization. Extensive experiments show that AnimalBooth consistently outperforms strong baselines on multiple benchmarks and improves both identity fidelity and perceptual quality.
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