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