  Most of the previous image-based 3D human pose and mesh estimation methods
estimate parameters of the human mesh model from an input image. However,
directly regressing the parameters from the input image is a highly non-linear
mapping because it breaks the spatial relationship between pixels in the input
image. In addition, it cannot model the prediction uncertainty, which can make
training harder. To resolve the above issues, we propose I2L-MeshNet, an
image-to-lixel (line+pixel) prediction network. The proposed I2L-MeshNet
predicts the per-lixel likelihood on 1D heatmaps for each mesh vertex
coordinate instead of directly regressing the parameters. Our lixel-based 1D
heatmap preserves the spatial relationship in the input image and models the
prediction uncertainty. We demonstrate the benefit of the image-to-lixel
prediction and show that the proposed I2L-MeshNet outperforms previous methods.
The code is publicly available https://github.com/mks0601/I2L-MeshNet_RELEASE.
