We offer an automatic method for creating a character's face that predicts both the shape of the face andtexture one portrait at a time. It can be used for most of the existing 3D games.
In order for 3D Morphing Face Models (3DMMs) to accurately reproduce the profile of a person, they must be trained on large sets of image and texture data.
Compiling these datasets may takequite a lot of time. Also, such a system can only work stably with the regular loading of new data. To overcome this limitation, the authors of the work, Lin, Yuan, and Zou, did not use generated photographs, but images of real people.
They first reconstructed the face based on3D face morphing model (3DMM) and convolutional neural networks (CNNs), and then transferring the shape of the 3D face to a grid of templates. As a result, the network receives a face image and an unrolled UV texture map as input, and then it predicts the light factors.
The authors tested their deep learning technique in a series of experiments: they compared the quality of the game characters with other generated models.
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