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联合注意力和条件GAN的被遮挡人体姿态和体形估计方法

Pose and Shape Estimation of Occluded Humans with Attention and Conditional GAN

  • 摘要: 基于图像的人体姿态和体形估计常常因人体被遮挡而充满挑战. 对此, 本文在提出两种策略的基础上设计出新的基于单幅图像的方法. 一个策略是设计多尺度的注意力模块, 输出具有丰富上下文信息的多尺度注意力特征, 可以有效的获得不受遮挡影响的全局的姿态和体形分布. 另外一个策略是基于热图的条件生成对抗网络, 将由关节热图得到的姿态估计作为约束, 实现进一步的网格精细调整. 借助这两个策略得到的姿态和体形估计方法实现全局预测和局部细节求精的结合, 实验结果表明该方法对身体部分被遮挡时有较好的重建效果.

     

    Abstract: The occlusions of body parts often appear in the images, which makes the human pose and shape estimation from single images difficult. This paper proposes a novel single-image oriented framework to tackle this problem, where two effective tactics are proposed. One is a multi-scale attention module which generates the enhanced multi-scale attention features with rich contextual information, so that efficient global pose and shape distribu-tion can be obtained without the affection of occlusion. The other is heatmap based conditional generative ad-versarial networks (GAN) which utilize the poses from the joint heatmaps as constraints and thus can refine the mesh of the occluded subject accurately. Combining these two tactics can make the proposed human pose and shape estimation method robustly recover the body meshes with both global prediction and local details. Qualita-tive and quantitative experiments show the efficiency of the proposed method for occluded humans.

     

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