The primary aim of human pose estimation involves accurately identifying key points on the human body, which is integral for various visual applications that require an in-depth understanding of human behavior. While high-resolution networks have excelled in this domain, their limitations, such as inadequate cross-dimensional information interaction and substantial computational costs, have prompted the need for more efficient solutions. To tackle these challenges, we introduce an adaptive cross-dimensional weighting high-resolution network (ACW-HRNet). This improved approach combines two key methods, cross-dimensional split convolution and adaptive context modeling (ACM). Cross-dimensional split convolution establishes effective cross-dimensional information exchange between spatial and channel, whereas ACM enhances the network’s ability to capture intricate spatial relationships through adaptive transformations and spatial weighting of input features. These make the network extract multi-scale context information and establish cross-dimensional dependencies, improving accuracy without introducing additional computational complexity. Our experiments on the COCO, MPII, and CrowdPose human pose estimation datasets illustrate its superior performance compared to mainstream lightweight networks.