Representation Learning of Vertex Heatmaps for 3D Human Mesh Reconstruction From Multi-View Images
Sung-Ho Chun, Sungbum Park, Ju Yong Chang
Abstract
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.