mainly use geometry reconstruction performance for evalu-
ation, which is our main focus.

6.1 Implementation Details
Network Architecture. For image feature extraction, we
adapt the same 2D image encoders in PIFu [8] (i.e., Hour-
glass Stack [62] for geometry and CycleGAN [63] for tex-
ture), which take as input an image of 512×512 resolution
and outputs a 256-channel feature map with a resolution
of 128×128. For volumetric feature extraction, we use a
3D convolution network which consists of two convolu-
tion layers and three residual blocks. Its input resolution
is 128×128×128 and its output is a 32-channel feature
volume with a resolution of 32×32×32. We replace batch
normalization with group normalization to improve the
training stability. The feature decoder is implemented as
a multi-layer perceptron, where the number of neurons is
(288, 1024, 512, 256, 128, ch), where ch = 1 for the geometry
network while ch = 4 for the texture network.
PaMIR- Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction