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Compared to joint position, the accuracy of joint rotation and shapeestimation has received relatively little attention in the skinned multi-personlinear model (SMPL)-based human mesh reconstruction from multi-view images. Thework in this field is broadly classified into two categories. The firstapproach performs joint estimation and then produces SMPL parameters by fittingSMPL to resultant joints. The second approach regresses SMPL parametersdirectly from the input images through a convolutional neural network(CNN)-based model. However, these approaches suffer from the lack ofinformation for resolving the ambiguity of joint rotation and shapereconstruction and the difficulty of network learning. To solve theaforementioned problems, we propose a two-stage method. The proposed methodfirst estimates the coordinates of mesh vertices through a CNN-based model frominput images, and acquires SMPL parameters by fitting the SMPL model to theestimated vertices. Estimated mesh vertices provide sufficient information fordetermining joint rotation and shape, and are easier to learn than SMPLparameters. According to experiments using Human3.6M and MPI-INF-3DHP datasets,the proposed method significantly outperforms the previous works in terms ofjoint rotation and shape estimation, and achieves competitive performance interms of joint location estimation.

Learnable human mesh triangulation for 3D human pose and shape estimation