  We propose FaceCom, a method for 3D facial shape completion, which delivers
high-fidelity results for incomplete facial inputs of arbitrary forms. Unlike
end-to-end shape completion methods based on point clouds or voxels, our
approach relies on a mesh-based generative network that is easy to optimize,
enabling it to handle shape completion for irregular facial scans. We first
train a shape generator on a mixed 3D facial dataset containing 2405
identities. Based on the incomplete facial input, we fit complete faces using
an optimization approach under image inpainting guidance. The completion
results are refined through a post-processing step. FaceCom demonstrates the
ability to effectively and naturally complete facial scan data with varying
missing regions and degrees of missing areas. Our method can be used in medical
prosthetic fabrication and the registration of deficient scanning data. Our
experimental results demonstrate that FaceCom achieves exceptional performance
in fitting and shape completion tasks. The code is available at
https://github.com/dragonylee/FaceCom.git.
