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.

UniHuman 是一个统一的模型，通过利用人类视觉编码器的指导和引入轻量级的姿势扭曲模块来提高模型的生成质量和泛化能力，并在真实环境中解决了人类图像编辑的多个方面。