论文标题
神经：3D人网训练集的神经注释仪
NeuralAnnot: Neural Annotator for 3D Human Mesh Training Sets
论文作者
论文摘要
大多数3D人类网状回归器都经过3D伪GT人体模型参数的全面监督，并且由于3D伪GTS带来了巨大的性能增长，因此用GT 2D/3D联合坐标对GT 2D/3D联合坐标进行了弱监督。 3D伪GT是通过注释者获得的，该系统在回归器的预处理阶段迭代拟合3D人体模型参数与GT 2D/3D联合坐标。最后一次拟合迭代时拟合的3D参数成为3D伪GTS，用于充分监督回归器。基于优化的注释者（例如Smplify-X）已被广泛用于获得3D伪GT。但是，它们通常会产生错误的3D伪GT，因为它们将3D参数符合每个样品的GT。为了克服限制，我们提出了基于神经网络的注释者NeuralAntot。神经诺特的主要思想是采用基于神经网络的回归剂，并将其专用于注释。假设没有3D伪GT可用，则神经植物受到训练集的GT 2D/3D联合坐标的弱监督。相同训练集的测试结果成为3D伪GT，用于充分监督回归器。我们表明，神经植物的3D伪GTS对训练回归器非常有益。我们公开提供了3D伪GTS。
Most 3D human mesh regressors are fully supervised with 3D pseudo-GT human model parameters and weakly supervised with GT 2D/3D joint coordinates as the 3D pseudo-GTs bring great performance gain. The 3D pseudo-GTs are obtained by annotators, systems that iteratively fit 3D human model parameters to GT 2D/3D joint coordinates of training sets in the pre-processing stage of the regressors. The fitted 3D parameters at the last fitting iteration become the 3D pseudo-GTs, used to fully supervise the regressors. Optimization-based annotators, such as SMPLify-X, have been widely used to obtain the 3D pseudo-GTs. However, they often produce wrong 3D pseudo-GTs as they fit the 3D parameters to GT of each sample independently. To overcome the limitation, we present NeuralAnnot, a neural network-based annotator. The main idea of NeuralAnnot is to employ a neural network-based regressor and dedicate it for the annotation. Assuming no 3D pseudo-GTs are available, NeuralAnnot is weakly supervised with GT 2D/3D joint coordinates of training sets. The testing results on the same training sets become 3D pseudo-GTs, used to fully supervise the regressors. We show that 3D pseudo-GTs of NeuralAnnot are highly beneficial to train the regressors. We made our 3D pseudo-GTs publicly available.
