  In this paper, we tackle the problem of 3D human shape estimation from single
RGB images. While the recent progress in convolutional neural networks has
allowed impressive results for 3D human pose estimation, estimating the full 3D
shape of a person is still an open issue. Model-based approaches can output
precise meshes of naked under-cloth human bodies but fail to estimate details
and un-modelled elements such as hair or clothing. On the other hand,
non-parametric volumetric approaches can potentially estimate complete shapes
but, in practice, they are limited by the resolution of the output grid and
cannot produce detailed estimates. In this work, we propose a non-parametric
approach that employs a double depth map to represent the 3D shape of a person:
a visible depth map and a "hidden" depth map are estimated and combined, to
reconstruct the human 3D shape as done with a "mould". This representation
through 2D depth maps allows a higher resolution output with a much lower
dimension than voxel-based volumetric representations. Additionally, our fully
derivable depth-based model allows us to efficiently incorporate a
discriminator in an adversarial fashion to improve the accuracy and "humanness"
of the 3D output. We train and quantitatively validate our approach on SURREAL
and on 3D-HUMANS, a new photorealistic dataset made of semi-synthetic in-house
videos annotated with 3D ground truth surfaces.
