  Reconstructing 3D shape from a single 2D image is a challenging task, which
needs to estimate the detailed 3D structures based on the semantic attributes
from 2D image. So far, most of the previous methods still struggle to extract
semantic attributes for 3D reconstruction task. Since the semantic attributes
of a single image are usually implicit and entangled with each other, it is
still challenging to reconstruct 3D shape with detailed semantic structures
represented by the input image. To address this problem, we propose 3DAttriFlow
to disentangle and extract semantic attributes through different semantic
levels in the input images. These disentangled semantic attributes will be
integrated into the 3D shape reconstruction process, which can provide definite
guidance to the reconstruction of specific attribute on 3D shape. As a result,
the 3D decoder can explicitly capture high-level semantic features at the
bottom of the network, and utilize low-level features at the top of the
network, which allows to reconstruct more accurate 3D shapes. Note that the
explicit disentangling is learned without extra labels, where the only
supervision used in our training is the input image and its corresponding 3D
shape. Our comprehensive experiments on ShapeNet dataset demonstrate that
3DAttriFlow outperforms the state-of-the-art shape reconstruction methods, and
we also validate its generalization ability on shape completion task.
