构建可双向推演的3D脸颅模型，支持医学重建与手术规划。
BFSM: 3D Bidirectional Face-Skull Morphable Model
- 基于200+样本建立跨模态脸颅数据集，含高精度扫描与CT
- 提出稠密射线匹配注册法，实现脸颅拓扑一致对齐
- 支持单图重建与个体化面部变化模拟，适合临床研究者使用
构建联合脸颅可变形模型在远程诊断、手术规划、医学教育和物理驱动面部仿真中具有巨大潜力。然而，受限于配对脸颅数据稀缺、配准精度不足及重建与临床应用探索有限，颅面畸形患者常被忽视，导致数据代表性不足。为此，我们首先构建了一个包含200余例样本的数据集，涵盖正常与罕见颅面畸形病例，每例包含基于CT的颅骨、基于CT的脸部以及高保真纹理人脸扫描。其次，提出一种新型稠密射线匹配注册方法，确保脸部、颅骨及其组织对应关系的拓扑一致性。基于此，我们引入3D双向脸颅可变形模型（BFSM），通过共享系数空间实现脸与颅骨间的双向形状推断，并建模组织厚度变化，支持同一颅骨生成多样面部形态，反映如脂肪随时间变化等个体差异。最后，我们在3D单图重建与手术规划预测中验证了模型潜力。大量实验表明方法具有鲁棒性与高精度。BFSM项目开源地址：https://github.com/wang-zidu/BFSM
原文摘要 · Abstract (English)
Building a joint face-skull morphable model holds great potential for applications such as remote diagnostics, surgical planning, medical education, and physically based facial simulation. However, realizing this vision is constrained by the scarcity of paired face-skull data, insufficient registration accuracy, and limited exploration of reconstruction and clinical applications. Moreover, individuals with craniofacial deformities are often overlooked, resulting in underrepresentation and limited inclusivity. To address these challenges, we first construct a dataset comprising over 200 samples, including both normal cases and rare craniofacial conditions. Each case contains a CT-based skull, a CT-based face, and a high-fidelity textured face scan. Secondly, we propose a novel dense ray matching registration method that ensures topological consistency across face, skull, and their tissue correspondences. Based on this, we introduce the 3D Bidirectional Face-Skull Morphable Model (BFSM), which enables shape inference between the face and skull through a shared coefficient space, while also modeling tissue thickness variation to support one-to-many facial reconstructions from the same skull, reflecting individual changes such as fat over time. Finally, we demonstrate the potential of BFSM in medical applications, including 3D face-skull reconstruction from a single image and surgical planning prediction. Extensive experiments confirm the robustness and accuracy of our method. BFSM is available at https://github.com/wang-zidu/BFSM
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