KB-DMGen: Knowledge-Based Global Guidance and Dynamic Pose Masking for Human Image Generation

Recent methods using diffusion models have made significant progress in human image generation with various control signals such as pose priors. In portrait generation, both the accuracy of human pose and the overall visual quality are crucial for realistic synthesis. Most existing methods focus on controlling the accuracy of generated poses, but ignore the quality assurance of the entire image. In order to ensure the global image quality and pose accuracy, we propose Knowledge-Based Global Guidance and Dynamic pose Masking for human image Generation (KB-DMGen). The Knowledge Base (KB) is designed not only to enhance pose accuracy but also to leverage image feature information to maintain overall image quality. Dynamic Masking (DM) dynamically adjusts the importance of pose-related regions. Experiments demonstrate the effectiveness of our model, achieving new state-of-the-art results in terms of AP and CAP on the HumanArt dataset. The code will be made publicly available.

Refinement Module based on Parse Graph of Feature Map for Human Pose Estimation

Parse graphs of the human body can be obtained in the human brain to help humans complete the human pose estimation (HPE). It contains a hierarchical structure, like a tree structure, and context relations among nodes. Many researchers pre-design the parse graph of body structure, and then design framework for HPE. However, these frameworks are difficulty adapting when encountering situations that differ from the preset human structure. Different from them, we regard the feature map as a whole, similarly to human body, so the feature map can be optimized based on parse graphs and each node feature is learned implicitly instead of explicitly, which means it can flexibly respond to different human body structure. In this paper, we design the Refinement Module based on the Parse Graph of feature map (RMPG), which includes two stages: top-down decomposition and bottom-up combination. In the top-down decomposition stage, the feature map is decomposed into multiple sub-feature maps along the channel and their context relations are calculated to obtain their respective context information. In the bottom-up combination stage, the sub-feature maps and their context information are combined to obtain refined sub-feature maps, and then these refined sub-feature maps are concatenated to obtain the refined feature map. Additionally ,we design a top-down framework by using multiple RMPG modules for HPE, some of which are supervised to obtain context relations among body parts. Our framework achieves excellent results on the COCO keypoint detection, CrowdPose and MPII human pose datasets. More importantly, our experiments also demonstrate the effectiveness of RMPG on different methods, including SimpleBaselines, Hourglass, and ViTPose.