CROSS REFERENCE TO RELATED APPLICATION

This non-provisional application claims priority of China patent application No. 202010709985.0, filed on 22 Jul. 2020, included herein by reference in its entirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The invention relates to facial reconstruction, and in particular, to a deep learning-based three-dimensional facial reconstruction system.

2. Description of the Prior Art

In computer vision, both the three-dimensional (3D) facial reconstruction technology and expression tracking technology are related to obtaining and establishing a face shape and appearance of a human face, and are used in the fields of facial recognition and expression driving. In general, the conventional 3D facial reconstruction technology and expression tracking technology have problems of low reconstruction accuracy and low expression discrimination, and as a consequence, additional signal processing of the reconstructed image has been used to obtain an accurate 3D reconstructed image.

SUMMARY OF THE INVENTION

According to one embodiment of the invention, a deep learning-based three-dimensional (3D) facial reconstruction system includes a main color range camera, a plurality of auxiliary color cameras, a processor and a memory. The main color range camera is arranged at a front angle of a reference user, and is used to capture a main color image and a main depth map of the reference user from the front angle. The plurality of auxiliary color cameras are arranged at a plurality of side angles of the reference user, and are used to capture a plurality of auxiliary color images of the reference user from the plurality of side angles. The processor is coupled to the main color range camera and the plurality of auxiliary color cameras. The memory is coupled to the processor and is used to store a plurality of instructions. The processor is used to execute the plurality of instructions to generate a 3D front angle image of a 3D ground truth model according to the main color image and the main depth map, generate a plurality of 3D side angle images of the 3D ground truth model according to the 3D front angle image and the plurality of auxiliary color images, and train an artificial neural network model according to a training image, the 3D front angle image and the plurality of 3D side angle images.

These and other objectives of the present invention will no doubt become obvious to those of ordinary skill in the art after reading the following detailed description of the preferred embodiment that is illustrated in the various figures and drawings.

