Skull-Conditioned Facial Soft-Tissue Reconstruction Using Anatomy-First Deep Volumetric Inference

Abstract

Skull-to-face reconstruction in forensic science is fundamentally ill-posed, as a single cranial structure may admit multiple anatomically valid facial soft-tissue realizations. Consequently, forensic facial reconstruction is not intended for definitive personal identification, but rather for generating anatomically plausible hypotheses under structural uncertainty. This paper presents an anatomy-first feasibility study for skull-conditioned facial soft-tissue reconstruction. The problem is formulated as a conditional volumetric inference task, in which facial morphology is predicted directly from cranial geometry using binary skull masks as the sole input. A three-dimensional U-Net architecture is implemented and evaluated without incorporating demographic attributes, semantic annotations, or appearance-based cues, enabling focused analysis of anatomically grounded inference from skeletal structure alone. Reconstruction performance is quantitatively assessed using volumetric overlap and surface-distance metrics on disjoint training, validation, and test datasets, and further examined through qualitative anatomical inspection. Results demonstrate consistent recovery of coarse facial morphology aligned with cranial constraints, while localized discrepancies arise in regions that are anatomically underdetermined by the skull. These variations reflect inherent biological variability rather than methodological failure. Overall, the findings support anatomy-first, skull-conditioned volumetric modeling as a principled, interpretable foundation for forensic facial reconstruction systems.