Abstract

3D face reconstruction from skull plays a crucial role in forensic identification and anthropology. Especially when landslides produce damaged or partially occluded human skulls, it is important to use 3D face reconstruction so that the family can identify the person. Conventional methods have been manual, subjective, and likely to take a long period of time. This paper proposes a system Skull2Face that receives the image of a skull, and generates a realistic 3D face structure using deep learning methods and tissue depth creation by using generative diffusion models. The method we propose combines anatomical landmarks with statistical modeling for accuracy while being able to create a detailed, personalized output with a realistic texture. This modular method lessens the dependence on large databases, introduces an efficient, faster, individualized, automated alternative methodology for facial reconstruction, and improves Identity-consistency, realism and accuracy over earlier methods of forensic facial reconstruction.

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