Exploring multi-level transformers with feature frame padding network for 3D human pose estimation

Recently, transformer-based architecture achieved remarkable performance in 2D to 3D lifting pose estimation. Despite advancements in transformer-based architecture they still struggle to handle depth ambiguity, limited temporal information, lacking edge frame details, and short-term temporal features. Consequently, transformer architecture encounters challenges in preciously estimating the 3D human position. To address these problems, we proposed Multi-Level Transformers with a Feature Frame Padding Network (MLTFFPN). To do this, we first propose the frame-padding network, which allows the network to capture longer temporal dependencies and effectively address the lacking edge frame information, enabling a better understanding of the sequential nature of human motion and improving the accuracy of pose estimation. Furthermore, we employ a multi-level transformer to extract temporal information from 3D human poses, which aims to improve the short-range temporal dependencies among keypoints of the human pose skeleton. Specifically, we introduce the Refined Temporal Constriction and Proliferation Transformer (RTCPT), which incorporates spatio-temporal encoders and a Temporal Constriction and Proliferation (TCP) structure to reveal multi-scale attention information and effectively addresses the depth ambiguity problem. Moreover, we incorporate the Feature Aggregation Refinement (FAR) module into the TCP block in a cross-layer manner, which facilitates semantic representation through the persistent interaction of queries, keys, and values. We extensively evaluate the efficiency of our method through experiments on two well-known benchmark datasets: Human3.6M and MPI-INF-3DHP.