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Implementing the transformer for global fusion is a novel and efficient method for pose estimation. Although the computational complexity of modeling dense attention can be significantly reduced by pruning possible human tokens, the accuracy of pose estimation still suffers from the problem of high overlap of candidate regions and severe background noise. Moreover, the undifferentiated fusion of features from different views also leads to a sizeable effective information loss. To address these challenges, we propose a Gated Region-Refine Pose Transformer (GRRPT) for human pose estimation. The proposed GRRPT can obtain the general area of the human body from the coarse-grained tokens and then embed it into the fine-grained ones to extract more details of the joints. Experimental results on COCO demonstrate that performing the Multi-Resolution Attention mechanism learns more refined candidate regions and improves accuracy. Furthermore, we design a Fusion Gate module consisting of two gates to pixel-wise select valid information from the auxiliary views, which significantly alleviates information redundancy. Finally, we evaluate the effectiveness of our method on Human3.6M and our dataset FDU-Motion and achieve state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Optical motion capture systems have been used intensively to obtain human body poses. However, there still exist several problems. First is the dislocation problem caused by joints being too close together. The second is the joint lost problem. Restricted by severe self-occlusions, cameras may not capture the target joints. Given this observation, we investigate the high-level constraints over human poses to solve these two problems. In this work, we present a Simplified-attention Enhanced Graph Convolutional Network (SaEGC-Net) to extract both spatial and temporal features from monocular videos flexibly. The SaEGC-Net for 3D human pose estimation is U-shaped and involves the Cascaded Spatial-Temporal Graph Convolutional (CST-GC) blocks and the Simplified Spatial-Temporal Attention (SST-Att) blocks, allowing for drawing long-range dependencies between unconnected joints by graph topologies and attention mechanism, respectively. Specifically, the CST-GC block embeds two predefined graph structures into a convolutional network, incorporating discriminative features from distant joints. The proposed SST-Att block disregards redundant information by sharing part of the attention map, which is highly lightweight. It also considers dimension-expanded joint relationships to maintain the diversity of dependence. To evaluate the effectiveness of our method, we conduct extensive experiments on two datasets: Human3.6M and our own dataset FDU-Motion. Results demonstrate that our model achieves excellent performance and can competently handle the above two problems. Also, ablation studies show that our network's submodules can better exploit the motion information of the human body. [ABSTRACT FROM AUTHOR]