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.

提出了一种基于 PaMIR 的框架，结合参数化身体模型与自由形式深度隐含函数，采用新型深度神经网络规范化自由形式深度隐含函数，通过训练损失并提出身体参考优化方法，以及增强参数化模型对隐含函数的一致性，实现在多种挑战性场景下的 3D 人体重建，并在多图像输入情况下取得了最新的性能表现。