![]() ![]() ![]() This article has discussed a new method for creating 3D face construction that automatically creates a game character faces from a single image. Next, is to download the CelebA-HQ dataset and put it in the required destination folder.The first step is to create a dataset by the following command.The output generated by MeInGame can be seen as : Now, put all the required files in their destination path. Pip install opencv-python fvcore h5py scipy scikit-image dlib face-alignment scikit-learn tensorflow-gpu=1.14.0 gast=0.2.2 Then follow the instructions from here, after cloning the MeInGame repository. Run the demo.py in the Deep3DFaceReconstruction folder to generate the required files.Os.environ+=":/content/Deep3DFaceReconstruction/renderer" Update the environment variables with all the new. !cp /content/FaceReconModel.pb /content/Deep3DFaceReconstruction/network/ !mkdir /content/Deep3DFaceReconstruction/network Lastly, download the pre-trained reconstruction model and put the files in a specific folder as shown below:.!cp /content/drive/MyDrive/rasterize_triangles_kernel.so /content/Deep3DFaceReconstruction/renderer/ %cp /content/Coarse_Dataset/Exp_Pca.bin /content/Deep3DFaceReconstruction/BFM/ Download the expression basis from here and unzip it to get the correct destination path’s required files.%cp /content/PublicMM1/01_MorphableModel.mat /content/Deep3DFaceReconstruction/BFM/ Unzip the BaseFaceModel and put it into the required path.Make sure to install TensorFlow 1.12 and tensorflow-gpu 1.12 for this step.!apt-key add /var/cuda-repo-9-0-local/7fa2af80.pub For the colab notebook, you can install cuda – 9.0 by these commands. Make sure to link the environment to CUDA 9.0.Expression Basis (transferred from Facewarehouse by Guo et al.): Following are the steps, to download all the required models for MeInGame.Basel Face Model 2009 (BFM09) : Download the model by signing up for the license.MeInGame is suitable for both Windows and Linux.This renderer tries to minimize the difference between the rendered image and the input image to get the desired results.Ĭomparison of MeInGame with other methods ![]() This coarse texture C is used to predict the lighting coefficients and refined texture map, which are then fed to the differential renderer. Then a coarse texture C is created by UV wrapping this input image on the game mesh. This 3DMM face is then passed to the game mesh. MeInGame takes the images as input and constructs a 3D character face on 3D Morphable Face Model (3DMM) and Convolutional Neural Networks (CNNs). Results have shown that the MeInGame can produce a game character similar to the input image and has succeeded in cutting out the effect of lightning and occlusions. Provides a shape transfer algorithm that can convert 3D Morphable Face Model (3DMM) mesh to games,.Provides cost-efficient facial texture acquisition.MeInGame introduces a novel pipeline for the training of 3D face reconstruction for games algorithms.The paper was submitted by Jiangke Lin, Yi Yuan, Zhengxia Zou and accepted at Association for the Advance of Artificial Intelligence (AAAI), 2021. Not long ago, researchers from Netease Fuxi AI Lab and the University of Michigan proposed a new state-of-the-art method in this direction – MeInGame: Create a Game Character Face from a Single Portrait, which automatically reconstructs a face from a single image. Though in the existing methods, game character customization methods require manual efforts from the users’ side to get the desired results. 3D face reconstruction has been widely used for gaming applications. ![]()
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