LangPose: Language-Aligned Motion for Robust 3D Human Pose Estimation

The introduction of LangPose marks a significant advancement in the field of 3D human pose estimation, particularly addressing the complexities of 2D-to-3D pose lifting, which has traditionally been hindered by depth ambiguity and occlusion. This framework leverages semantic information by aligning motion embeddings with text embeddings of fine-grained action labels, enabling it to better interpret and reconstruct poses even in challenging scenarios. LangPose operates in two stages: pretraining, where it learns to recognize actions and reconstruct 3D poses from noisy 2D inputs, and fine-tuning, which refines its capabilities using real-world datasets. The results are promising, with LangPose achieving a mean per joint position error (MPJPE) of 36.7 mm on the Human3.6M dataset and 15.5 mm on the MPI-INF-3DHP dataset, showcasing its robustness and effectiveness in real-world applications. This development not only enhances the accuracy of pose estimation but also opens new avenues for ap…