GSHumanMesh: Gaussian Splatting with Surface Alignment for Human Mesh Reconstruction

The reconstruction of 3D digital humans has extensive practicality in various fields at present, including virtual reality, augmented reality, film and game production, visual effects, and so on. Recently, 3D Gaussian Splatting (3DGS) has achieved high-quality novel view synthesis and fast rendering and can also be applied to human reconstruction. However, it is very difficult to directly edit the reconstructed human model. Therefore, it is necessary to extract the geometric shape of the human body from the Gaussian model. However, since the optimized Gaussian model is unorganized, it produces a noisy and unrealistic surface. Thus, accurately extracting the mesh from the Gaussian model remains a challenge. To address this issue, the authors propose GSHumanMesh, which extracts a mesh from Gaussians with surface alignment for human mesh reconstruction. They utilize a set of multi-view human images as the input and obtain the Gaussian model via 3DGS. They also propose an improved surface regularization term based on SuGaR and 2DGS to enforce Gaussians to be surface-aligned. Then they extract an accurate mesh from the Gaussians using Poisson reconstruction. Additionally, the authors propose post-processing to refine the reconstructed mesh and make it smoother. The method was tested on the THUman4.0 and DualGS datasets. Compared with other state-of-the-art methods based on 3DGS, the resulting reconstruction is smoother, more accurate, and shows more intricate details.