ICFNet: Interactive-complementary Fusion Network for Monocular 3D Human Pose Estimation

Most existing methods for 3D human pose estimation from monocular images focus on learning the spatial correlation of either the global or local joints of the human body but fail to adequately capture the inherent dependencies between them. To address this limitation, we propose the Interactive Complementary Fusion Network (ICFNet), an algorithm designed to fully utilize the prior knowledge of both global and local joint relationships to enhance prediction performance. Specifically, we introduce two feature capturers: the Global Knowledge Prior Capturer (GKPC) and the Local Region Subject Capturer (LRSC), which respectively capture global body knowledge and local joint information. Additionally, we propose three joint constraint mechanisms to express the potential association dependencies between global and local joints, which are further modeled using two association capturers: the Refined-Regression Association Capture Module (RR-ACM) and the Generalized-Guidance Association Capture Module (GG-ACM). Moreover, we introduce a novel feature transformation module, the Link Conversion Module (LCM), to transform and augment pose features. The algorithm adopts a complementary process to enhance the interaction and fusion of global and local feature information by gradually imposing constraints on the physical topological features of the human body, thereby improving its modeling capabilities. Extensive experiments demonstrate that our proposed ICFNet achieves state-of-the-art results on two challenging benchmark datasets: Human 3.6M and MPI-INF-3DHP. The code and model are available at: https://github.com/PENG-LAU/ICFNet.