  3D human pose estimation can be handled by encoding the geometric
dependencies between the body parts and enforcing the kinematic constraints.
Recently, Transformer has been adopted to encode the long-range dependencies
between the joints in the spatial and temporal domains. While they had shown
excellence in long-range dependencies, studies have noted the need for
improving the locality of vision Transformers. In this direction, we propose a
novel pose estimation Transformer featuring rich representations of body joints
critical for capturing subtle changes across frames (i.e., inter-feature
representation). Specifically, through two novel interaction modules;
Cross-Joint Interaction and Cross-Frame Interaction, the model explicitly
encodes the local and global dependencies between the body joints. The proposed
architecture achieved state-of-the-art performance on two popular 3D human pose
estimation datasets, Human3.6 and MPI-INF-3DHP. In particular, our proposed
CrossFormer method boosts performance by 0.9% and 0.3%, compared to the closest
counterpart, PoseFormer, using the detected 2D poses and ground-truth settings
respectively.
