Abstract—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
PaMIR- Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction