Deducing the 3D face from a skull is an essential but challenging task in forensic science and archaeology. Existing methods for automated facial reconstruction yield inaccurate results, suffering from the non-determinative nature of the problem that a skull with a sparse set of tissue depth cannot fully determine the skinned face. Additionally, their texture-less results require further post-processing stages to achieve a photo-realistic appearance. This paper proposes an end-to-end 3D face reconstruction and exploration tool, providing textured 3D faces for reference. With the help of state-of-the-art text-to-image diffusion models and image-based facial reconstruction techniques, we generate an initial reference 3D face, whose biological profile aligns with the given skull. We then adapt these initial faces to meet the statistical expectations of extruded anatomical landmarks on the skull through an optimization process. The joint statistical distribution of tissue depths is learned on a small set of anatomical landmarks on the skull. To support further adjustment, we propose an efficient face adaptation tool to assist users in tuning tissue depths, either globally or at local regions, while observing plausible visual feedback. Experiments conducted on a real skull-face dataset demonstrated the effectiveness of our proposed pipeline in terms of reconstruction accuracy, diversity, and stability.

翻译：从颅骨推断三维人脸是法医学和考古学中一项必要但具有挑战性的任务。现有自动化面部重建方法由于问题的不确定性（稀疏组织深度数据无法完全确定蒙皮人脸）而产生不准确的结果。此外，这些方法的无纹理结果需要进一步后处理才能实现逼真外观。本文提出了一种端到端的三维人脸重建与探索工具，提供带纹理的三维人脸作为参考。借助最新的文生图扩散模型和基于图像的面部重建技术，我们生成初始参考三维人脸，其生物学特征与给定颅骨对齐。随后通过优化过程，将这些初始人脸调整为符合颅骨上凸出解剖标志点的统计预期。组织深度的联合统计分布通过学习颅骨上一小组解剖标志点获得。为支持进一步调整，我们提出了一种高效的人脸适配工具，帮助用户全局或局部调整组织深度，同时观察合理的视觉反馈。在真实颅骨-人脸数据集上的实验证明了所提方法在重建精度、多样性和稳定性方面的有效性。