GRPose: Learning Graph Relations for Human Image Generation with Pose Priors

• URL: http://arxiv.org/abs/2408.16540v3
• Date: Fri, 27 Dec 2024 09:27:39 GMT
• Title: GRPose: Learning Graph Relations for Human Image Generation with Pose Priors
• Authors: Xiangchen Yin, Donglin Di, Lei Fan, Hao Li, Wei Chen, Xiaofei Gou, Yang Song, Xiao Sun, Xun Yang,
• Abstract summary: We propose a framework that delves 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. A pose perception loss is introduced based on a pretrained pose estimation network to minimize the pose differences.
• Score: 21.91374799527015
• License:
• Abstract: Recent methods using diffusion models have made significant progress in human image generation with various control signals such as pose priors. However, existing efforts are still struggling to generate high-quality images with consistent pose alignment, resulting in unsatisfactory output. In this paper, we propose a framework that delves 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. Besides, a pose perception loss is 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 clearly demonstrate that our model can achieve significant performance improvement over the latest benchmark models. The code is available at \url{https://xiangchenyin.github.io/GRPose/}.

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