Single-picture three-dimensional face reconstruction method based on cascade regression network

Abstract

The invention discloses a single-picture three-dimensional face reconstruction method based on a cascade regression network. The whole single-picture three-dimensional face reconstruction method includes the steps of 1, data preparation, wherein three-dimensional face data is collected, and a three-dimensional face-shape point cloud of a face picture is obtained; 2, model designing, wherein a network model comprising two-stage multitask convolutional neural networks is established, face features are regressed step by step, and then modified shape features are extracted; 3, model training, wherein the two-stage multitask convolutional neural networks are trained respectively, and a multitask cascade regression convolutional neural network M is obtained; 4, model testing, wherein an image Iis input into the multitask cascade regression convolutional neural network M, and model testing is carried out. According to the single-picture three-dimensional face reconstruction method based on the cascade regression network, the multitask cascade regression convolutional neural network M is used, a three-dimensional face shape, face feature points and a face posture are regressed step by step, and meanwhile a traditional feature extracting method is improved; by computing visibility of the feature points, the confusion performance of the feature points on a two-dimensional face picture is eliminated, and the single-picture three-dimensional face reconstruction method is more robust for large gestures.

Description

technical field [0001] The invention relates to a three-dimensional human face reconstruction method, in particular to a single-picture three-dimensional human face reconstruction method based on a cascade regression network. Background technique [0002] The goal of 3D face reconstruction is to reconstruct the corresponding 3D face shape from the input 2D face image. This technology is widely used in the fields of facial animation, facial expression analysis and 3D face recognition. The current representative methods for 3D face reconstruction are: [0003] (1) A single image 3D face reconstruction method based on a 3D face model. The main technical means are: based on a 2D face database, the SDM (Supervised Descent Method) algorithm is used to train the 2D face feature point parameters. Model. When performing face reconstruction based on a single frontal photo, first use the two-dimensional picture feature point parameter model to extract face feature points; then, acco...

AI Technical Summary

Problems solved by technology

The disadvantages of this method are: some facial feature points are not visible in large poses, which affects the accuracy of the parameter solution of the 3D face deformation model; on the other hand, this method only uses the in

Images