AnimeAdapter: 细粒度且一致的零样本动漫人物生成
AnimeAdapter: A Modular Adapter for Appearance-Consistent Anime Character Generation
AI总结：
本文提出了一种轻量级的外观适配器，用于在多样编辑条件下实现可控且一致的动漫人物生成，通过注入单张参考图像的细粒度视觉特征到扩散过程中，并结合CLIP的局部空间化特性，开发出语义选择性局部注意力机制，进一步解耦人物外观与空间布局，从而实现高效的动漫人物生成。
摘要：
我们提出了一种轻量级的外观适配器，用于在多样编辑条件下实现可控且一致的动漫人物生成。与依赖大规模视觉-语言模型或针对特定主体的微调不同，我们的方法将单张参考图像的细粒度视觉特征注入扩散过程。基于CLIP的局部空间化特性，我们开发了语义选择性局部注意力机制。为了进一步解耦人物外观与空间布局，我们在适配器训练过程中引入姿态感知的条件。所得到的预训练适配器保持紧凑、模块化，并且完全兼容Stable Diffusion社区工作流程，同时在部署时不需要额外的微调。此外，我们还提出了一个基于精选和重构的Danbooru提示的高质量动漫人物数据集，并在多个实际的人物编辑场景中评估了我们的方法。我们的代码、模型权重和数据集将在接受后公开发布。
英文摘要：
We present a lightweight appearance adapter for Stable Diffusion that enables controllable and consistent anime character generation under diverse editing conditions. Instead of relying on large-scale vision-language models or per-subject fine-tuning, our method injects fine-grained visual features from a single reference image into the diffusion process. Based on CLIP emergent local spatialization, we develop semantic-selective local attention. To further disentangle character appearance from spatial layout, we incorporate pose-aware conditioning during adapter training. The resulting pretrained adapter remains compact, modular, and fully compatible with Stable Diffusion community workflows, while requiring no additional fine-tuning at deployment time. Furthermore, we present a high-quality anime character dataset based on curated and restructured Danbooru prompts, and evaluate our method across several practical character editing scenarios. Our code, model weights, and dataset will be publicly released upon acceptance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
