InferTrans: Hierarchical structural fusion transformer for crowded human pose estimation

Human pose estimation in crowded scenes presents unique challenges due to frequent occlusions and complex interactions between individuals. To address these issues, we introduce InferTrans, a hierarchical structural fusion Transformer designed to improve crowded human pose estimation. InferTrans integrates semantic features into structural information using a hierarchical joint-limb-semantic fusion module. By reorganizing joints and limbs into a tree structure, the fusion module facilitates effective information exchange across different structural levels, and leverage both global structural information and local contextual details. Furthermore, we explicitly model limb structural patterns separately from joints, treating limbs as vectors with defined lengths and orientations. This allows our model to infer complete human poses from minimal input, significantly enhancing pose refinement tasks. Extensive experiments on multiple datasets demonstrate that InferTrans outperforms existing pose estimation techniques in crowded and occluded scenarios. The proposed InferTrans serves as a robust post-processing technique, and is capable of improving the accuracy and robustness of pose estimation in challenging environments.