融合双通道关节约束的三维人体姿态估计

3D Human Pose Estimation Incorporating Dual-Channel Joint Constraints

针对三维人体姿态估计中末端关节存在较大估计误差的问题,以时空图卷积网络为基础,提出融合双通道关节约束的三维人体姿态估计方法(JC-STGNet)。提出自适应权重模块,解耦不同关节特征变换,提高特征表达灵活性;结合人体先验结构知识,设计两个新颖的卷积核,获取更丰富的关节特征;最后,引入关节内部约束模块,对自由度较高的末端关节进行特征替换,提高模型对末端关节的估计准确度。在Human3.6M数据集上的实验表明,与基线时空图卷积网络相比,JC-STGNet在MPJPE和P-MPJPE两个指标下,准确度分别提升了3.1 mm和12.4 mm;与同准确度的网络相比,本文的参数量减少了22.27%。

To tackle the issue of significant estimation errors in the distal joints of 3D human pose estimation,this paper presents a method called joint-constrained spatio-temporal graph network(JC-STGNet)that incorporates dual-channel joint constraints.Firstly,it introduces an adaptive weight module to separate different joint feature transformations,thereby enhancing the flexibility of feature representation.Moreover,the paper designs two novel convolutional kernels that leverage prior structural knowledge of the human body to capture more informative joint features.Lastly,it introduces an intra-joint constraint module to replace features in the distal joints with higher degrees of freedom,thereby improving the accuracy of the model’s estimation for these joints.Experimental results on the Human3.6M dataset demonstrate that JC-STGNet achieves a 3.1 mm and 12.4 mm improvement in accuracy,as measured by the MPJPE and P-MPJPE metrics,respectively,compared to the baseline spatio-temporal graph network.Furthermore,this proposed method reduces the pa-rameter count by 22.27%compared to networks with similar accuracy.