  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.
