Full Face Texture Generation of Virtual Human

Face texture completion plays a significant role in virtual human research, and the quality of face texture needs to be improved urgently. One of the major obstacles to single-face texture generation is that the generated textures are always incomplete for self-occlusion of the input face, and the other is that pixel details are limited by the illumination. To address this, we propose a method for complete face texture generation based on generative adversarial networks. The face parameters obtained from 3D Morphable Model are processed as conditional vectors in the encoder, and the multivariate Gaussian distribution of the latent code is used in the networks to learn the complete texture features. We established a face texture dataset CFT for training the network. Meanwhile, we show the effectiveness of the proposed approach in qualitative and quantitative experiments. The visual results under different tasks show superior performances compared with the state-of-the-art approaches.