This paper tackles the problem of estimating 3D body shape of clothed humans
from single polarized 2D images, i.e. polarization images. Polarization images
are known to be able to capture polarized reflected lights that preserve rich
geometric cues of an object, which has motivated its recent applications in
reconstructing surface normal of the objects of interest. Inspired by the
recent advances in human shape estimation from single color images, in this
paper, we attempt at estimating human body shapes by leveraging the geometric
cues from single polarization images. A dedicated two-stage deep learning
approach, SfP, is proposed: given a polarization image, stage one aims at
inferring the fined-detailed body surface normal; stage two gears to
reconstruct the 3D body shape of clothing details. Empirical evaluations on a
synthetic dataset (SURREAL) as well as a real-world dataset (PHSPD) demonstrate
the qualitative and quantitative performance of our approach in estimating
human poses and shapes. This indicates polarization camera is a promising
alternative to the more conventional color or depth imaging for human shape
estimation. Further, normal maps inferred from polarization imaging play a
significant role in accurately recovering the body shapes of clothed people.

该研究论文提出了一种针对服装人体的三维形状估计方法，通过使用极化图像捕获几何线索来重构人体表面法线图，并采用深度学习模型将其转化为 3D 人体形态估计，其结果表明极化相机对于人体形态估计是一种有前途的替代方法。

从偏振图像中重建三维人体形状

3D Human Shape Reconstruction from a Polarization Image

Human shape estimation is an important task for video editing, animation and
fashion industry. Predicting 3D human body shape from natural images, however,
is highly challenging due to factors such as variation in human bodies,
clothing and viewpoint. Prior methods addressing this problem typically attempt
to fit parametric body models with certain priors on pose and shape. In this
work we argue for an alternative representation and propose BodyNet, a neural
network for direct inference of volumetric body shape from a single image.
BodyNet is an end-to-end trainable network that benefits from (i) a volumetric
3D loss, (ii) a multi-view re-projection loss, and (iii) intermediate
supervision of 2D pose, 2D body part segmentation, and 3D pose. Each of them
results in performance improvement as demonstrated by our experiments. To
evaluate the method, we fit the SMPL model to our network output and show
state-of-the-art results on the SURREAL and Unite the People datasets,
outperforming recent approaches. Besides achieving state-of-the-art
performance, our method also enables volumetric body-part segmentation.

本篇研究提出了一种名为 BodyNet 的神经网络，通过直接推断体积棱柿，从而预测自然图像中的三维人体形状，同时结合三维损失和多视角重投影损失以及适当的监督，实现了性能的提升；通过将 SMPL 模型应用于网络输出，并在 SURREAL 和 Unite the People 数据集上展示了最先进的结果，证明了本方法的有效性。