Texture and Normal Map Estimation for 3D Face Reconstruction

3D Morphable Models (3DMMs) can effectively capture human face shape and texture by exploiting face statistics computed on 3D scanned faces. However, the capacity of these models imposes limitations on shape details and texture expressiveness. Consequently, they provide low-detailed texture and normal map estimations. We propose a novel framework designed to estimate high-quality texture and normal maps in the UV space of a single input image. This framework comprises encoder, normalization and decoder models. To this end, we aim to learn rich and robust representations that remain unaffected under changing pose, expression and illumination by enhancing estimation details. Experimental results demonstrate that our framework outperforms the baseline significantly in terms of realism and identity preservation.