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.

翻译：近年来，定制化生成技术展现出巨大潜力，该技术仅需使用3-5张用户提供的图像训练模型，即可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（如模仿人体姿态）的细粒度控制仍缺乏深入研究。本文提出PersonificationNet，能够控制指定主体（如卡通角色或毛绒玩具）模仿给定参考人物图像的姿态。该网络包含定制化分支、姿态条件分支和结构对齐模块。具体而言：首先，定制化分支模拟指定主体的外观特征；其次，姿态条件分支将人体结构信息迁移至变体实例；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，我们提出的PersonificationNet性能优于现有最先进方法。