PMDI: Combining Parametric-Model and Depth-Aware Implicit Function for Single-View Human Reconstruction

3D human reconstruction from a single image has achieved great progress with recent deep neural networks. However, conventional approaches still struggle with the issues of over-smoothing details and wrong limb poses. To this end, we propose PMDI, a method that combines parametric-model and depth-aware implicit function for single-view human reconstruction. First, given an RGB image of a person's whole body as input, the method predicts its corresponding SMPL parameter model, depth map, and front (back) normal map by using deep neural networks. Then, the predicted front depth map and normal feature are used as the additional parameters of the deep implicit function for reconstructing coarse results. Finally, the fine result is produced by integrating its corresponding coarse result with detailed back D-BiNI surface. Extensive experiments on the current large publicly available dataset (including DeepHuman and THUman2.0) demonstrate that PMDI outperforms the state-of-the-arts including PIFu, PIFuHD,PaMIR, and ICON.