Human-Structure-Aware Token Position Embedding for Tokenized Pose Estimation

Zejun Gu

doi:10.1109/TIP.2026.3700936

Human-Structure-Aware Token Position Embedding for Tokenized Pose Estimation

2026-06-11

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DOI：10.1109/TIP.2026.3700936

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Tokenized pose estimation (TPE) has demonstrated remarkable performance in lightweight human pose estimation (HPE) models. However, existing TPE methods typically initialize keypoint tokens randomly, without explicitly incorporating human structure priors. These priors play a vital role in HPE by effectively mitigating common challenges such as occlusion and ambiguity. To this end, we propose a Structure-Aware Keypoint Position Embedding (SAKPE). This embedding explicitly encodes inherent structural properties of the human body, such as symmetry and order, into the positional coordinates of keypoint tokens. It also employs learnable scale and offset factors to adapt to diverse human poses, thereby fully exploiting the geometric constraints among keypoints. Furthermore, to better leverage the positional relationships among patch tokens, we introduce a Layer-adaptive Hybrid Patch Position Embedding (LHPPE). It dynamically fuses absolute and relative position embeddings of patch tokens based on attention distributions across Transformer layers, enabling the model to learn both absolute and relative positional information adaptively. Taking the two together, we propose a novel position embedding method for pose estimation, named Human-structure-aware Token Position Embedding (HTPE). It significantly improves the performance of various TPE models. Extensive experiments on COCO, CrowdPose, and OCHuman show that HTPE achieves state-of-the-art (SOTA) performance among lightweight methods, with a negligible increase in parameters and FLOPs. Notably, it demonstrates consistent improvements under occlusion,, achieving up to 3.3 AP gains. The source code can be found in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/guzejungithub/HTPE</uri>

Keywords:

Lightweight human pose estimation

human-structure-aware

hybrid patch position embedding

Human-Structure-Aware Token Position Embedding for Tokenized Pose Estimation

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