Personalized image generation has been significantly advanced, enabling the creation of highly realistic and customized images. However, existing methods often struggle with generating images of multiple people due to occlusions and fail to accurately personalize full-body shapes. In this paper, we propose PersonaCraft, a novel approach that combines diffusion models with 3D human modeling to address these limitations. Our method effectively manages occlusions by incorporating 3D-aware pose conditioning with SMPLx-ControlNet and accurately personalizes human full-body shapes through SMPLx fitting. Additionally, PersonaCraft enables user-defined body shape adjustments, adding flexibility for individual body customization. Experimental results demonstrate the superior performance of PersonaCraft in generating high-quality, realistic images of multiple individuals while resolving occlusion issues, thus establishing a new standard for multi-person personalized image synthesis. Project page: https://gwang-kim.github.io/persona_craft

本研究解决了个性化图像生成中多人物生成的困难，尤其是遮挡和全身形状个性化的不足。通过结合扩散模型和3D人体建模的方法，提出了PersonaCraft，能够有效处理遮挡问题并精准个性化全身形状，实验结果显示其在生成高质量多人物图像方面表现优越，推动了多人物个性化图像合成的新标准。

PersonaCraft：基于单一参考的多身份个性化全身图像合成