| dc.description.abstract | Nowadays, due to the maturity of technology and the perfection of instruments and equipment, the application of many industries has gradually expanded from two-dimensional plane to three-dimensional space, such as video entertainment industry, face recognition, medical cosmetology and other related fields, in order to realize a more realistic environment and provide excellent sensory experience for the public.
Traditional face reconstruction algorithms are unable to learn real face features in depth and rely on low-level information of the image, while the software modeling method and instrument modeling method used by the industry are time-consuming and costly. Therefore, in recent years, 3D face reconstruction techniques based on deep learning have shown effective results in terms of quality and efficiency, balancing quality and time to reconstruct 3D face models corresponding to images.
Therefore, this paper uses a weakly-supervised approach that combines a Convolutional Neural Network and a 3D Morphable Face Model. The face can be effectively reconstructed without realistic labels by using the depth feature extraction and regress coefficients of the image, and improving the pre-processing and loss function. The reconstruction results will be evaluated and analyzed in two datasets, and the reconstruction results will be demonstrated. | en_US |