PersonificationNet：让定制化主体像人一样行动
PersonificationNet: Making customized subject act like a person
AI总结：
针对定制化生成中主体姿态细粒度控制不足的问题，提出含定制化分支、姿态条件分支和结构对齐模块的PersonificationNet，可让指定主体复刻参考人物姿态，性能优于现有最优方法。
摘要：
近年来，定制化生成技术展现出巨大潜力，它仅需用户提供3-5张图像即可训练模型，合成指定主体的新图像。尽管后续应用提升了定制化生成的灵活性与多样性，但对给定主体实现类人姿态的细粒度控制仍缺乏研究。本文提出PersonificationNet，可控制卡通角色、毛绒玩具等指定主体做出与给定参考人物图像一致的姿态。该网络包含定制化分支、姿态条件分支和结构对齐模块：首先，定制化分支模拟指定主体的外观；其次，姿态条件分支将人体结构信息迁移至不同类型的实例；最后，结构对齐模块在推理阶段弥合人体与指定主体之间的结构差异。实验结果表明，本文提出的PersonificationNet性能优于现有最优方法。
英文摘要：
Recently customized generation has significant potential, which uses as few as 3-5 user-provided images to train a model to synthesize new images of a specified subject. Though subsequent applications enhance the flexibility and diversity of customized generation, fine-grained control over the given subject acting like the person's pose is still lack of study. In this paper, we propose a PersonificationNet, which can control the specified subject such as a cartoon character or plush toy to act the same pose as a given referenced person's image. It contains a customized branch, a pose condition branch and a structure alignment module. Specifically, first, the customized branch mimics specified subject appearance. Second, the pose condition branch transfers the body structure information from the human to variant instances. Last, the structure alignment module bridges the structure gap between human and specified subject in the inference stage. Experimental results show our proposed PersonificationNet outperforms the state-of-the-art methods.
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。
