Human pose estimation refers to accurately estimating the position of the human body from a single RGB image and detecting the location of the body. It serves as the basis for several computer vision tasks, such as human tracking, 3D reconstruction, and autonomous driving. Improving the accuracy of pose estimation has significant implications for the advancement of computer vision. This paper addresses the limitations of single-branch networks in pose estimation. It presents a top-down single-target pose estimation approach based on multi-branch self-calibrating networks combined with graph convolutional neural networks. The study focuses on two aspects: human body detection and human body pose estimation. The human body detection is for athletes appearing in sports competitions, followed by human body pose estimation, which is divided into two methods: coordinate regression-based and heatmap test-based. To improve the accuracy of the heatmap test, the high-resolution feature map output from HRNet is used for deconvolution to improve the accuracy of single-target pose estimation recognition.

This study presents a hybrid BRDF modeling framework combining a five-parameter physical model with a (20,20,20) Multilayer Perceptron (MLP) model network to address the critical challenge of accurate grazing-angle prediction for low-gloss black coatings (SB-3A, Z306, PNC). While the baseline parametric model achieves 1.2 RMSE at 80° incidence. The proposed MLP model-enhanced solution reduces these high-angle errors to <0.012 RMSE while maintaining <5-min computational efficiency. Comprehensive validation shows the framework’s universality across materials with apparent anisotropy indices (ASI) of 0.465–30.26. The work demonstrates that neural networks can optimally compensate for missing physics in traditional models without sacrificing interpretability, offering immediate industrial value for aerospace coating analysis.