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.

翻译：我们提出FaceCom，一种三维面部形状补全方法，能够为任意形式的不完整面部输入提供高保真结果。与基于点云或体素的端到端形状补全方法不同，我们的方法依赖于一个易于优化的基于网格的生成网络，使其能够处理不规则面部扫描的形状补全任务。我们首先在一个包含2405个身份的三维面部混合数据集上训练形状生成器。基于不完整的面部输入，我们在图像修复引导下通过优化方法拟合完整面部。补全结果通过后处理步骤进行细化。FaceCom展示了有效且自然地补全具有不同缺失区域和缺失程度的面部扫描数据的能力。我们的方法可用于医疗假体制造以及缺损扫描数据的配准。实验结果表明，FaceCom在拟合和形状补全任务中取得了卓越的性能。代码发布于 https://github.com/dragonylee/FaceCom.git。