4 METHOD
4.1 Overview
Our PaMIR-based 3D human reconstruction is implemented
as a neural network. An overview of the network is illus-
trated in Fig.2. Given a single image I as input, our method
ﬁrst feeds it into the GCMR network to estimate an initial
SMPL model, which is then converted into an occupancy
volume through voxelization. In the feature extraction step,
the input image is encoded into a feature map FI by a
2D convolution network, while the occupancy volume are
encoded into a feature volume FV by a 3D convolution
network. For each point in the 3D space, its pixel-aligned im-
age feature S (FI , π(p)) and voxel-aligned volume feature
S (FV , p) are sampled in the feature map and the feature
volume, respectively. The two feature vectors are then con-
catenated and translated to an occupancy probability value
by a feature-to-occupancy decoder as formulated in Eqn.(2)
and (5).
PaMIR- Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction