In this study, a three-dimensional (3D) facial UV texture restoration algorithm based on gated convolution is proposed to address the texture loss caused by self-occlusion in unconstrained facial images captured from large viewing angles during reconstructing 3D facial structures from a single image. First, a gated convolution mechanism is designed to learn a dynamic feature selection approach for each channel and spatial position, thereby enhancing the network's ability to capture complex nonlinear features. These gated convolutions are then stacked to form an encoder-decoder network that repairs 3D facial UV texture images with irregular defects. In addition, a spectral normalization loss function is introduced to stabilize the generative adversarial network, and a segmented training approach is implemented to overcome the challenges of cost and accessibility in collecting 3D facial texture datasets. The experimental results show that the proposed algorithm outperforms mainstream algorithms in terms of the peak signal-to-noise ratio and structural similarity. The proposed algorithm effectively restores UV texture maps under large angle occlusion, yielding more comprehensive facial texture maps with natural, coherent pixel restoration, and realistic texture details.