More importantly, conditioning the non-parametric repre-
sentation on SMPL models raises a crucial problem: how can
we obtain accurate SMPL model estimation given a single
image at testing time? In DeepHuman, the SMPL models
at testing time are estimated using learning-based methods
and/or optimization-based methods. Although the recent
years have witness a renaissance in the context of single-
image body shape estimation [10], [11], [12], [13], [14], [15],
we observe that there is still an accuracy gap between the
training and testing stages: during training, we can obtain
well-aligned SMPL models for the training data by ﬁtting
the SMPL template to the high quality scans, while for
testing, the SMPL estimations provided by the state-of-the-
art single-image methods [12], [13] still cannot align with
the keypoints or the silhouettes as perfectly as the SMPL
models in the training dataset.
PaMIR- Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction