Occlusion presents a significant challenge in human pose estimation. The challenges posed by occlusion can be attributed to the following factors: 1) Data: The collection and annotation of occluded human pose samples are relatively challenging. 2) Feature: Occlusion can cause feature confusion due to the high similarity between the target person and interfering individuals. 3) Inference: Robust inference becomes challenging due to the loss of complete body structural information. The existing methods designed for occluded human pose estimation usually focus on addressing only one of these factors. In this paper, we propose a comprehensive framework DAG (Data, Attention, Graph) to address the performance degradation caused by occlusion. Specifically, we introduce the mask joints with instance paste data augmentation technique to simulate occlusion scenarios. Additionally, an Adaptive Discriminative Attention Module (ADAM) is proposed to effectively enhance the features of target individuals. Furthermore, we present the Feature-Guided Multi-Hop GCN (FGMP-GCN) to fully explore the prior knowledge of body structure and improve pose estimation results. Through extensive experiments conducted on three benchmark datasets for occluded human pose estimation, we demonstrate that the proposed method outperforms existing methods. Code and data will be publicly available.

翻译：遮挡对人体姿态估计构成了重大挑战。遮挡带来的困难可归因于以下因素：1）数据：遮挡人体姿态样本的采集与标注相对困难；2）特征：目标人物与干扰个体之间的高度相似性会导致特征混淆；3）推理：由于完整身体结构信息的缺失，鲁棒推理变得困难。现有针对遮挡人体姿态估计的方法通常仅针对其中某一因素进行改进。本文提出综合框架DAG（数据、注意力、图），旨在解决遮挡导致的性能退化问题。具体而言，我们引入掩码关节实例粘贴数据增强技术来模拟遮挡场景；同时提出自适应判别注意力模块（ADAM）有效增强目标个体特征；此外，提出特征引导多跳图卷积网络（FGMP-GCN）充分探索身体结构先验知识以提升姿态估计效果。在三个遮挡人体姿态估计基准数据集上的大量实验表明，所提方法优于现有方法。代码与数据将公开提供。