(a)(b)(c)JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015

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Fig. 13. Qualitative evaluation on depth-ambiguity-aware training loss:
(a) the input images, (b) the corresponding SMPL models estimated by
GCMR, (c) our reconstruction results, (d) the reconstruction results of
the baseline network.

the ﬁgure, with our depth-ambiguity-aware reconstruction
loss, the network is able to learn to reconstruct 3D human
models that have consistent poses with the estimated SMPL
models. On the contrary, the models reconstructed by the
baseline network without our depth-ambiguity-aware loss
are less consistent with the SMPL models and have more
artifacts. The quantitative study is presented in Tab.3. From
the numeric comparison between the 3rd and 4th rows
of Tab.3, we can conclude that our depth-ambiguity-aware
loss avoids the negative impact of the depth inconsistency
between predicted SMPL models and ground-truth scans,
and improves the accuracy of the reconstruction results.
PaMIR- Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction