Align Before You Correct: Surface-Aligned Evidence for 3D Face Refinement

Refining an existing 3D face reconstruction requires choosing where corrective evidence should be represented. Camera-ray corrections vary with viewpoint, while coefficient updates depend on a specific face model. We therefore express the correction on the reconstructed surface. We project each surface point into the calibrated views, where visible appearance and viewing geometry are fused to predict a signed displacement along the local normal. A robust parametric fit converts these displacements into face-model coefficients. Surface displacements are easier to predict than both ray-space targets. On held-out FaceScape identities, the advantage is smallest for frontal views and largest for oblique ones. Residual correlation is 0.39 for ray projection and 0.48 for ray intersection, compared with 0.52 on the surface using one view and 0.74 using five. With five views, symmetric Chamfer error drops by 4.5% from a strong start and by 8.5% from an image-predicted start. This recovers half of the reduction achieved by ground-truth displacements through the same fit and roughly twice the gain from averaging one-view predictions. Direct coefficient regression from the same evidence produces about one fifth of the gain from the image-predicted start, while population-mean, constant, and random displacement fields provide no comparable improvement. Without retraining, the same predictor reduces error by 9.5–11.7% for four published reconstructors and a multi-view landmark fit on FaceScape and by 4.3% for Pixel3DMM on Headspace. Expressing correction on the reconstructed surface therefore allows the same image evidence to transfer across calibrated rigs, face models, and starting reconstructors.