Recent methods using diffusion models have made significant progress in human image generation with various additional controls such as pose priors. However, existing approaches still struggle to generate high-quality images with consistent pose alignment, resulting in unsatisfactory outputs. In this paper, we propose a framework delving into the graph relations of pose priors to provide control information for human image generation. The main idea is to establish a graph topological structure between the pose priors and latent representation of diffusion models to capture the intrinsic associations between different pose parts. A Progressive Graph Integrator (PGI) is designed to learn the spatial relationships of the pose priors with the graph structure, adopting a hierarchical strategy within an Adapter to gradually propagate information across different pose parts. A pose perception loss is further introduced based on a pretrained pose estimation network to minimize the pose differences. Extensive qualitative and quantitative experiments conducted on the Human-Art and LAION-Human datasets demonstrate that our model achieves superior performance, with a 9.98% increase in pose average precision compared to the latest benchmark model. The code is released on *******.

本研究解决了现有基于扩散模型的人像生成方法在姿势一致性方面的不足。我们提出了一种利用姿势先验的图关系框架，通过构建姿势先验与扩散模型潜在表示之间的图拓扑结构，学习姿势部位间的内在关联。实验结果表明，该方法在姿势平均精度上相比最新基准模型提高了9.98%。

GRPose：通过姿势先验学习人像生成的图关系