training with the learning rate of 1 × 10−3, the batch size of
3, the number of epochs of 10, and the number of sampled
points of 5000 per subject. The learning rate is decayed
by the factor of 0.1 at every 10000-th iteration. We also
combine predicted and ground-truth SMPL models during
network training. Speciﬁcally, in each batch, we randomly
select one image to replace the predicted SMPL model with
the ground-truth when constructing the depth-ambiguity-
aware reconstruction loss. In this way we can guarantee the
best performance is obtained once the underlining SMPL
becomes more accurate. The multi-image network is ﬁne-
tuned from the models trained for single-view network with
three random views of the same subject using a learning rate
of 2 × 10−5 and a batch size of 1. Before training the PaMIR
network, we ﬁrst ﬁne-tune the pre-trained GCMR network
on our training set.
PaMIR- Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction