GRPose: Learning Graph Relations for Human Image Generation with Pose Priors
<|reference_start|>GRPose: Learning Graph Relations for Human Image Generation with Pose Priors: 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 *******.<|reference_end|>
@article{yin2024grpose:,
    title={GRPose: Learning Graph Relations for Human Image Generation with Pose
  Priors},
    author={Xiangchen Yin, Donglin Di, Lei Fan, Hao Li, Chen Wei, Xiaofei Gou,
  Yang Song, Xiao Sun, Xun Yang},
    journal={arXiv preprint arXiv:2408.16540},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.16540},
    primaryClass={cs.CV}
}