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-Object Interaction (HOI) detection lies at the core of action
understanding. Besides 2D information such as human/object appearance and
locations, 3D pose is also usually utilized in HOI learning since its
view-independence. However, rough 3D body joints just carry sparse body
information and are not sufficient to understand complex interactions. Thus, we
need detailed 3D body shape to go further. Meanwhile, the interacted object in
3D is also not fully studied in HOI learning. In light of these, we propose a
detailed 2D-3D joint representation learning method. First, we utilize the
single-view human body capture method to obtain detailed 3D body, face and hand
shapes. Next, we estimate the 3D object location and size with reference to the
2D human-object spatial configuration and object category priors. Finally, a
joint learning framework and cross-modal consistency tasks are proposed to
learn the joint HOI representation. To better evaluate the 2D ambiguity
processing capacity of models, we propose a new benchmark named Ambiguous-HOI
consisting of hard ambiguous images. Extensive experiments in large-scale HOI
benchmark and Ambiguous-HOI show impressive effectiveness of our method. Code
and data are available at this https URL

本论文提出了一种详细的 2D-3D 联合表示学习方法，能够使计算机更好地理解人物和物体之间的交互行为，效果在大规模的 HOI 基准和 Ambiguous-HOI 数据集上表现良好。