  It is challenging to directly estimate the human geometry from a single image
due to the high diversity and complexity of body shapes with the various
clothing styles. Most of model-based approaches are limited to predict the
shape and pose of a minimally clothed body with over-smoothing surface. While
capturing the fine detailed geometries, the model-free methods are lack of the
fixed mesh topology. To address these issues, we propose a novel
topology-preserved human reconstruction approach by bridging the gap between
model-based and model-free human reconstruction. We present an end-to-end
neural network that simultaneously predicts the pixel-aligned implicit surface
and an explicit mesh model built by graph convolutional neural network.
Experiments on DeepHuman and our collected dataset showed that our approach is
effective. The code will be made publicly available.
