ForensicFace: Reliable 3D Face Texture Reconstruction via Confidence Guided Diffusion

Reliable 3D face texture reconstruction from a single 2D facial image is of critical importance in criminal investigations. Existing methods usually use visible texture directly as a generative condition, but they overlook differences in observation confidence across facial regions. As a result, unreliable projections may be retained incorrectly, while genuine local details such as moles and skin tone variations may be smoothed or rewritten during generation. To address these problems, we present ForensicFace, a framework for reliable single image 3D face texture reconstruction using a canonical UV representation. First, we propose texel level observation confidence estimation to model the reliability of input texture continuously, addressing the inability of binary visibility masks to distinguish the reliability of different observed regions. We then design an observation confidence guided diffusion model that controls generative freedom according to observation confidence. Regions with high observation confidence preserve more of the input observation, whereas regions with low observation confidence or no direct observation rely more on the generative prior for completion. This design prevents reliable observations from being overwritten by the generative prior while reducing the risk of directly retaining unreliable projections. Finally, we estimate generation confidence from consistency across repeated stochastic samples to characterize the stability of generated regions. Experiments on FaceScape and Stirling ESRC show that ForensicFace outperforms existing methods in PSNR, SSIM, LPIPS, and CSIM while maintaining stable texture reconstruction quality under large pose variations.