Accepted in: The 10th International Conference on Image and Graphics(ICIG2019)-Oral
In 'Demo.py' file, you will find how to run these codes. In 'FaceSwap/Demo2.py' file, you will find how to run face swap code.
In this paper proposes a novel and efficient end-to-end 3D face alignment framework.We build an efficient and stable network model through Depthwise Separable Convolution and Densely Connected Convolutional,named MobDenseNet. Simultaneously,different loss functions are used to constrain 3D parameters based on 3D Morphable Model (3DMM) and 3D vertices
If you have any question about this code, feel free to reach me(ljiang_jnu@outlook.com)