We tackle the problem of monocular 3D reconstruction of articulated objects
like humans and animals. We contribute DensePose 3D, a method that can learn
such reconstructions in a weakly supervised fashion from 2D image annotations
only. This is in stark contrast with previous deformable reconstruction methods
that use parametric models such as SMPL pre-trained on a large dataset of 3D
object scans. Because it does not require 3D scans, DensePose 3D can be used
for learning a wide range of articulated categories such as different animal
species. The method learns, in an end-to-end fashion, a soft partition of a
given category-specific 3D template mesh into rigid parts together with a
monocular reconstruction network that predicts the part motions such that they
reproject correctly onto 2D DensePose-like surface annotations of the object.
The decomposition of the object into parts is regularized by expressing part
assignments as a combination of the smooth eigenfunctions of the
Laplace-Beltrami operator. We show significant improvements compared to
state-of-the-art non-rigid structure-from-motion baselines on both synthetic
and real data on categories of humans and animals.

本文提出了一种名为 DensePose 3D 的方法，通过弱监督机器学习从二维图像注释中实现基于单目视觉的带关节物体（例如人和动物）的三维重建，同时通过利用 Laplace-Beltrami 算子的平滑特征函数将物体分解成部分，再预测它们的运动以在 2D DensePose-like 表面注释中正确地重影，与基于非刚性结构运动的最新基线方法相比，在合成和真实数据上对人和动物进行了显著改进。