  Modeling 3D humans accurately and robustly from a single image is very
challenging, and the key for such an ill-posed problem is the 3D representation
of the human models. To overcome the limitations of regular 3D representations,
we propose Parametric Model-Conditioned Implicit Representation (PaMIR), which
combines the parametric body model with the free-form deep implicit function.
In our PaMIR-based reconstruction framework, a novel deep neural network is
proposed to regularize the free-form deep implicit function using the semantic
features of the parametric model, which improves the generalization ability
under the scenarios of challenging poses and various clothing topologies.
Moreover, a novel depth-ambiguity-aware training loss is further integrated to
resolve depth ambiguities and enable successful surface detail reconstruction
with imperfect body reference. Finally, we propose a body reference
optimization method to improve the parametric model estimation accuracy and to
enhance the consistency between the parametric model and the implicit function.
With the PaMIR representation, our framework can be easily extended to
multi-image input scenarios without the need of multi-camera calibration and
pose synchronization. Experimental results demonstrate that our method achieves
state-of-the-art performance for image-based 3D human reconstruction in the
cases of challenging poses and clothing types.
