We propose an automatic face and head deformation method that combines 3D faces with an arbitrary head model. With the rapid development of Computer Vision and Deep Learning, 3d scans of human faces are becoming easier to obtain. How to complete the scanned 3D face data and make it a complete full head model, or give the scanned 3D face different 3D hairstyles, has always been an open question. For this reason, we propose a Global Deformation Model (GDM) which is implemented by multiple iterations. By building a Global Deformation Model (GDM), a full-head 3D data with complex hairstyles could be combined with 3D face data. In this way, the scanned face is automatically completed as a full-head model. Experiments show that compared with other deformation algorithms and full-head reconstruction methods, our method has better automation and robustness. It shows good deformation results in complex 3D data. We provide an attractive solution for graphic design, Virtual Reality, 3D printing, and other industries, which can be widely used in consumer scenes.
Boundary and edge cues are very useful in improving various visual tasks, such as semantic segmentation, object recognition, stereo vision, and object generation. In recent years, the issue of edge detection has been revisited, and deep learning has made significant progress. The traditional edge detection is a challenging two-category problem, and the Multi-category semantic edge detection is a more challenging problem. And we model the edge detection of cultural relics and classify the pixels of cultural relics. To this end, we propose a novel end-to-end deep semantic edge learning architecture based on ResNet. Then, we proposed an adaptive class weighter for this problem to supervise the training. The results show that the proposed architecture is superior to the existing semantic edge detection methods in our own design of cultural relic edge detection performance.