LaplacianFusion

Abstract

We propose LaplacianFusion , a novel approach that reconstructs detailed and controllable 3D clothed-human body shapes from an input depth or 3D point cloud sequence. The key idea of our approach is to use Laplacian coordinates, well-known differential coordinates that have been used for mesh editing, for representing the local structures contained in the input scans, instead of implicit 3D functions or vertex displacements used previously. Our approach reconstructs a controllable base mesh using SMPL, and learns a surface function that predicts Laplacian coordinates representing surface details on the base mesh. For a given pose, we first build and subdivide a base mesh, which is a deformed SMPL template, and then estimate Laplacian coordinates for the mesh vertices using the surface function. The final reconstruction for the pose is obtained by integrating the estimated Laplacian coordinates as a whole. Experimental results show that our approach based on Laplacian coordinates successfully reconstructs more visually pleasing shape details than previous methods. The approach also enables various surface detail manipulations, such as detail transfer and enhancement.

A single-image human body reconstruction method combining normal recovery and frequency domain completion

“…This frequency domain processing method on the grid has also been applied to the human body reconstruction field. Kim et al [37] used Laplacian coordinates to represent the local structure of the input grid. This method uses the SMPL model to reconstruct a controllable base grid and learns a surface function using a neural network.…”