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.

本文提出了一种基于深度图的非参数方法，用于从单个 RGB 图像中估计人体的 3D 形状，并结合对抗训练进行优化，并在真实数据集上进行了验证。

人体建模：从单张图像非参数三维人体形状估计

Moulding Humans: Non-parametric 3D Human Shape Estimation from Single  Images

Recent advances in 3D human shape estimation build upon parametric
representations that model very well the shape of the naked body, but are not
appropriate to represent the clothing geometry. In this paper, we present an
approach to model dressed humans and predict their geometry from single images.
We contribute in three fundamental aspects of the problem, namely, a new
dataset, a novel shape parameterization algorithm and an end-to-end deep
generative network for predicting shape.
First, we present 3DPeople, a large-scale synthetic dataset with 2.5 Million
photo-realistic images of 80 subjects performing 70 activities and wearing
diverse outfits. Besides providing textured 3D meshes for clothes and body, we
annotate the dataset with segmentation masks, skeletons, depth, normal maps and
optical flow. All this together makes 3DPeople suitable for a plethora of
tasks.
We then represent the 3D shapes using 2D geometry images. To build these
images we propose a novel spherical area-preserving parameterization algorithm
based on the optimal mass transportation method. We show this approach to
improve existing spherical maps which tend to shrink the elongated parts of the
full body models such as the arms and legs, making the geometry images
incomplete.
Finally, we design a multi-resolution deep generative network that, given an
input image of a dressed human, predicts his/her geometry image (and thus the
clothed body shape) in an end-to-end manner. We obtain very promising results
in jointly capturing body pose and clothing shape, both for synthetic
validation and on the wild images.

本文提出了一种从单张照片中预测着装人体几何形状的新方法，包括三个关键点：一个新数据集，一个新的形状参数化算法和一个端到端的深度生成网络。