ComposeAnyone：基于解耦多模态条件的可控布局到人体生成
ComposeAnyone: Controllable Layout-to-Human Generation with Decoupled Multimodal Conditions
AI总结：
提出ComposeAnyone，一种基于解耦多模态条件的可控布局到人体生成方法，支持文本或参考图像控制手绘布局的任意部分，并引入ComposeHuman数据集，实验证明其生成对齐性和可控性优于现有方法。
摘要：
在扩散模型成功的基础上，多模态图像生成任务取得了显著进展。其中，人体图像生成已成为一项有前景的技术，有望彻底改变时装设计流程。然而，现有方法往往仅专注于基于文本到图像或图像参考的人体生成，无法满足日益复杂的需求。为解决人体生成中灵活性和精确性的局限，我们提出了ComposeAnyone，一种具有解耦多模态条件的可控布局到人体生成方法。具体而言，我们的方法允许使用文本或参考图像对手绘人体布局中的任意部分进行解耦控制，并在生成过程中无缝集成。手绘布局利用色块几何形状（如椭圆和矩形），易于绘制，为定义空间布局提供了更灵活、更易用的方式。此外，我们引入了ComposeHuman数据集，该数据集为每张人体图像的不同组件提供了解耦的文本和参考图像注释，从而支持人体图像生成任务中更广泛的应用。在多个数据集上的大量实验表明，ComposeAnyone生成的人体图像与给定布局、文本描述和参考图像具有更好的对齐性，展示了其多任务能力和可控性。
英文摘要：
Building on the success of diffusion models, significant advancements have been made in multimodal image generation tasks. Among these, human image generation has emerged as a promising technique, offering the potential to revolutionize the fashion design process. However, existing methods often focus solely on text-to-image or image reference-based human generation, which fails to satisfy the increasingly sophisticated demands. To address the limitations of flexibility and precision in human generation, we introduce ComposeAnyone, a controllable layout-to-human generation method with decoupled multimodal conditions. Specifically, our method allows decoupled control of any part in hand-drawn human layouts using text or reference images, seamlessly integrating them during the generation process. The hand-drawn layout, which utilizes color-blocked geometric shapes such as ellipses and rectangles, can be easily drawn, offering a more flexible and accessible way to define spatial layouts. Additionally, we introduce the ComposeHuman dataset, which provides decoupled text and reference image annotations for different components of each human image, enabling broader applications in human image generation tasks. Extensive experiments on multiple datasets demonstrate that ComposeAnyone generates human images with better alignment to given layouts, text descriptions, and reference images, showcasing its multi-task capability and controllability.
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。
