  This study addresses the problem of 3D human mesh reconstruction from
multi-view images. Recently, approaches that directly estimate the skinned
multi-person linear model (SMPL)-based human mesh vertices based on volumetric
heatmap representation from input images have shown good performance. We show
that representation learning of vertex heatmaps using an autoencoder helps
improve the performance of such approaches. Vertex heatmap autoencoder (VHA)
learns the manifold of plausible human meshes in the form of latent codes using
AMASS, which is a large-scale motion capture dataset. Body code predictor (BCP)
utilizes the learned body prior from VHA for human mesh reconstruction from
multi-view images through latent code-based supervision and transfer of
pretrained weights. According to experiments on Human3.6M and LightStage
datasets, the proposed method outperforms previous methods and achieves
state-of-the-art human mesh reconstruction performance.
