UniHuman：一个用于自然场景人物图像编辑的统一模型
UniHuman: A Unified Model for Editing Human Images in the Wild
AI总结：
UniHuman提出一个统一的人物图像编辑模型，通过视觉编码器引导和轻量级姿态变形模块联合处理姿态、服装和文本编辑任务，并在40万图文对训练下显著超越任务特定模型。
摘要：
人物图像编辑包括改变人物姿态、更换服装或根据文本提示编辑图像等任务。然而，以往的工作通常分别处理这些任务，忽视了联合学习带来的相互促进优势。本文提出UniHuman，一个在真实场景中处理人物图像编辑多个方面的统一模型。为提升模型的生成质量和泛化能力，我们利用人类视觉编码器的引导，并引入一个轻量级姿态变形模块，该模块能够利用不同的姿态表示，适应未见过的纹理和图案。此外，为弥合现有人物编辑基准与真实世界数据之间的差距，我们整理了40万对高质量的人物图像-文本对用于训练，并收集了2000张人物图像用于域外测试，两者均涵盖多样的服装风格、背景和年龄群体。在域内和域外测试集上的实验表明，UniHuman以显著优势超越了任务特定模型。在用户研究中，UniHuman在平均77%的情况下受到用户偏好。我们的项目可在https://github.com/NannanLi999/UniHuman获取。
英文摘要：
Human image editing includes tasks like changing a person's pose, their clothing, or editing the image according to a text prompt. However, prior work often tackles these tasks separately, overlooking the benefit of mutual reinforcement from learning them jointly. In this paper, we propose UniHuman, a unified model that addresses multiple facets of human image editing in real-world settings. To enhance the model's generation quality and generalization capacity, we leverage guidance from human visual encoders and introduce a lightweight pose-warping module that can exploit different pose representations, accommodating unseen textures and patterns. Furthermore, to bridge the disparity between existing human editing benchmarks with real-world data, we curated 400K high-quality human image-text pairs for training and collected 2K human images for out-of-domain testing, both encompassing diverse clothing styles, backgrounds, and age groups. Experiments on both in-domain and out-of-domain test sets demonstrate that UniHuman outperforms task-specific models by a significant margin. In user studies, UniHuman is preferred by the users in an average of 77% of cases. Our project is available at https://github.com/NannanLi999/UniHuman.
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。
