CN113284249B - Multi-view three-dimensional human body reconstruction method and system based on graph neural network - Google Patents
Multi-view three-dimensional human body reconstruction method and system based on graph neural network Download PDFInfo
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Abstract
The invention discloses a multi-view three-dimensional human body reconstruction method and a system based on a graph neural network, wherein the method comprises the steps of collecting human body images after a human body is placed in the center of an annular camera; acquiring a plurality of images, selecting a reference image from the images, and recording the rest images as source images; extracting the features of the image, inputting the features into an attention network of the image so as to obtain parameters of a parameterized human body model, and reconstructing a human body three-dimensional model in the output of the parameterized human body model by using the output parameters; and (3) re-projecting the obtained parameterized human body model onto each image, aligning, and projecting color and texture information in the image onto the triangulated vertex of the model by using a computer graphics technology to finish texture mapping. The method utilizes the graph neural network to complete the extraction of key points and skin vertex information in the parameterized human body model, and can realize the fine human body three-dimensional reconstruction work.
Description
Technical Field
The invention relates to the technical field of computer vision, in particular to a multi-view three-dimensional human body reconstruction method and a system based on a graph neural network.
Background
The development of the computer vision field has been promoted by the rise and application of neural networks in the past years, and many machine learning tasks that had been heavily dependent on manual feature extraction have been changed by various deep learning neural networks. Recently, people are more interested in the expansion of the deep learning method on the graph, and researchers design a neural network structure-a graph neural network aiming at graph data on the idea of using a convolutional network, a cyclic network and a deep automatic encoder for reference.
On the other hand, human three-dimensional reconstruction has been a research focus in the field of computer vision. The three-dimensional reconstruction method has two main reconstruction modes, namely, three-dimensional reconstruction is performed by using a single image with depth information, but the single image method has less information amount and cannot completely restore a three-dimensional scene, and the other method is three-dimensional reconstruction after stereo matching is performed on images shot from multiple visual angles, and various methods for extracting depth information based on depth learning already exist.
How to introduce the neural network into the human body three-dimensional reconstruction so as to realize more accurate human body three-dimensional reconstruction is a technical problem which is urgently needed to be solved at present.
Disclosure of Invention
In order to solve the technical problem of how to realize more accurate human body three-dimensional reconstruction, the invention provides a multi-view three-dimensional human body reconstruction method and system based on a graph neural network.
Therefore, the multi-view three-dimensional human body reconstruction method based on the graph neural network provided by the invention comprises the following steps:
a2, when a human body is placed in the center of the annular camera, acquiring a human body image;
a3, acquiring a plurality of images, selecting a reference image from the images, and recording the rest images as source images;
a4, constraining each reference image and the source images of two sides of each reference image positioned in the middle of the adjacent reference images by using polar line constraint conditions in multi-view geometry, searching to obtain a plurality of groups of skeleton point coordinates of which the number and the positions accord with the definition of a parameterized human body model, averaging the plurality of groups of skeleton point coordinates to obtain a final group of joint coordinates, extracting characteristic points on the surface of the human body to generate human body shape sparse point clouds serving as surface contour mesh preset vertexes of the parameterized human body model;
a5, selecting partial point clouds around the skeleton points, associating the skeleton points with the selected point clouds to form natural image data, inputting image attention neural network training to obtain the posture information parameters and body type information parameters of a parameterized human body model, and reconstructing a human body three-dimensional model in the output of the parameterized human body model by utilizing output parameters;
and A6, re-projecting the obtained parameterized human body model onto each image, aligning, and projecting color and texture information in the image onto a triangulated vertex of the model by using a computer graphics technology to complete texture mapping.
Further, before the step a2, a step a1 is further included, and the step a1 specifically includes erecting a plurality of cameras in a circular arrangement around the human body capture area, and calibrating the plurality of cameras.
Further, calibrating the plurality of cameras specifically includes performing internal-participation external reference calibration on the cameras by a black-and-white chess-card-grid calibration method.
Further, in the step a4, feature points of the human body surface are extracted by using a scale-invariant feature transform algorithm.
Further, in the step a5, a part of the point cloud around the bone point is extracted by using a K-nearest neighbor method.
Further, in the step a5, the key point information of the posture information is obtained through the graph automatic coding neural network, and then the key point information and the triangulated vertex composition graph are input into the graph attention neural network, and the body type information in the parameterized human body model is obtained through learning.
Further, in the step a5, the graph attention neural network is implemented by a graph attention layer stack, and the attention calculation coefficient of each graph attention layer is:
wherein alpha isijIs the attention coefficient, N, from the vertex j to the bone point iiAll neighboring vertices, W, representing bone points iijThe weight coefficient between the vertex j and the bone point i, i.e. the L2 norm, is represented, a represents the weight vector, and LeakyReLU represents the LeakyReLU activation function.
Further, in step a6, color information in the pixels of the video is extracted by using a computer graphics technique, and the extracted color information is re-projected onto the skin vertex of the parameterized human body model, so as to complete texture mapping.
Further, the parameterized human body model is selected as a skin multi-person linear model, the skin multi-person linear model comprises two parameters of attitude information theta and body type information beta, the attitude information theta is defined by 24 human body key points, and the body type information beta is 10 parameters for controlling the change of the shape of the human body and is expressed as a triangulated skin vertex forming the surface of the model.
The multi-view three-dimensional human body reconstruction system based on the graph neural network comprises a camera, a processor and a memory, wherein a program which can be operated by the processor is stored in the memory, and the processor can realize the multi-view three-dimensional human body reconstruction method based on the graph neural network by operating the program.
Compared with the prior art, the invention has the following beneficial effects:
in the process of applying the graph neural network to human body three-dimensional reconstruction, the graph neural network is utilized to complete the extraction of key points and skin vertex information in the parameterized human body model, and the fine human body three-dimensional reconstruction work can be realized.
In some embodiments of the invention, the following advantages are also provided:
the parameterized human body model is selected as a skin multi-person linear model, and the SMPL (skin multi-person linear) human body three-dimensional reconstruction model can be well imported into Unity, Maya and other application programs, so that the multi-view three-dimensional human body reconstruction method based on the graph neural network can be used for manufacturing animations, virtual reality and the like.
Drawings
Fig. 1 is a flow chart of a multi-view three-dimensional human body reconstruction method based on a graph neural network.
Detailed Description
In order to more clearly understand the technical features, objects, and effects of the present invention, embodiments of the present invention will now be described with reference to the accompanying drawings.
As shown in fig. 1, the multi-view three-dimensional human body reconstruction method based on the graph neural network provided by the embodiment of the invention includes the following steps:
a1, marking N cameras as 0,1,2 … … N-1, erecting the N cameras around a human body capturing area in an annular arrangement mode, and calibrating the N cameras by using a traditional camera calibration method to obtain internal reference and external reference information of the cameras. In the invention, 15 industrial cameras can be used and respectively marked as 0,1 and 2 … … 14, the 15 industrial cameras are placed on an experimental site, the height of the cameras is about 1.2 meters away from the ground, the horizontal distance between every two adjacent cameras is about 0.8-0.9 meter, and the cameras are circularly arranged according to the marked sequence. Or a circle can be drawn by taking the center point of the human body capturing area as the center of the circle and taking about 2 meters as the radius, and the camera can be erected at the equal division point after the circle 15 is divided equally. Specifically, in practical application, the number of cameras, the erection height, the shooting range size and the like can be set according to the requirements of experimenters. Calibrating the N cameras to calibrate the internal parameters and the external parameters of the cameras, firstly printing a black-and-white checkerboard to be attached to a plane as a calibration object, respectively shooting 15 different pictures of the checkerboard by adjusting the direction of the calibration object for each Camera by using the Camera to be calibrated, preferably, guiding the 15 pictures into a Camera calibration tool box in MATLAB, setting the size of the checkerboard, extracting angular points and calculating five internal parameters and six external parameters of the cameras.
And A2, when the human body is placed at the center of the annular camera, acquiring the human body image. When the human body is arranged at the position of a circle center surrounded by the centers of the annular cameras, the calibrated 15 cameras are used for shooting the human body simultaneously to finish image acquisition.
And A3, processing the acquired N images to convert the acquired N images into RGB type images, selecting three images acquired by three cameras with the labels of 0, N/3 and 2N/3 as three reference images respectively in the images, and marking the rest images as source images. Specifically, the function in the OpenCV open source library is used to process and convert the picture into an RGB image, the human body images acquired by the cameras 0,5 and 10 are respectively recorded as reference images, and the human body images acquired by the other cameras are recorded as source images.
A4, constraining each reference image and a source image of which two sides are located in the middle of an adjacent reference image by using polar line constraint conditions in multi-view geometry, searching to obtain three groups of skeleton point coordinates of which the number and the positions conform to those defined by an SMPL (skin multi-person linear) model, averaging the three groups of skeleton point coordinates to obtain a final group of joint coordinates, extracting feature points on the surface of a human body by using an SIFT algorithm to generate human body shape sparse point clouds serving as surface contour mesh preset vertexes of the SMPL human body three-dimensional reconstruction model. Specifically, corresponding skeleton point positions are marked on reference images acquired by cameras with the labels of 0,5 and 10 according to 24 skeleton point positions in an SMPL (simple Markov chain) human body model, the three-dimensional space position coordinates of the skeleton points are searched for the No. 0 image and the images with the labels of 11,12,13,14,1,2,3 and 4 by using an epipolar constraint condition, and the three-dimensional space position coordinates of the skeleton points are searched for the No. 5 and No. 10 images and 8 source images adjacent to the two sides of the No. 5 and No. 10 images in the same manner. And averaging the three groups of obtained skeleton point coordinates to obtain final skeleton point coordinates. Preferably, a SIFT algorithm (scale invariant feature transform algorithm) is used to obtain a sparse point cloud of the human body surface contour in the image, and the point cloud can be used as a preset vertex of the skin surface of the SMPL model. Specifically, the SMPL human body three-dimensional reconstruction model comprises two parameters of posture information theta and body type information beta, wherein the posture information theta is defined by 24 human body key points, and the body type information beta is 10 parameters for controlling the change of the human body shape and is expressed as a triangularized skin vertex forming the surface of the model. The SMPL model may be imported into a Unity or like graphics program.
A5, selecting partial point clouds around the skeleton points by a K neighbor method, associating the skeleton points with the selected point clouds to form natural graph data, inputting GAT graph attention neural network training to obtain attitude information parameters and body type information parameters of the SMPL model, and reconstructing a human body three-dimensional model in SMPL output by using the output parameters. Preferably, a regression matrix is used to convert the three-dimensional coordinate representation of the bone points into a representation in the SMPL model, each bone point vector containing information on three angles of limb rotation (yaw, roll, pitch), and in particular, the regression matrix can be trained using an open source data set such as sureral or human3.6 m. Further, at least 50 point cloud positions are selected around each bone point by using a K-nearest neighbor method, the bone points and the selected point clouds are combined into natural map data, and then the natural map data are trained by using a GAT (goal-oriented) graph attention neural network. Wherein the GAT graph attention network is realized by stacking graph attention layers, and the attention calculation coefficient of each graph attention layer is as follows:
wherein alpha isijIs the attention coefficient, N, from the vertex j to the bone point iiAll neighboring vertices, W, representing bone points iijAnd a represents a weight coefficient between the vertex j and the bone point i, namely an L2 norm, and a represents a weight vector, and finally, after normalization processing is carried out by using a softmax function, nonlinearity is provided by using an LeakyReLU activation function. The advantage of using this structure is that the neighbor pairs of the skeleton point and the vertex can be computed in parallel, so that the operation efficiency is high, and the structure can process nodes with different degrees and assign corresponding weights to the neighbors. In addition, the GAT graph can realize training without knowing the structure of the whole graph, and SMPL human model parameters can be obtained through fast and accurate inductive learning. And obtaining posture parameters and body type parameters of the SMPL model after GAT graph attention network training of graph data formed by the skeletal points and the human body contour vertexes, and inputting the posture parameters and the body type parameters into the SMPL model to obtain a final SMPL human body model. Specifically, key point information of the posture information is obtained through a GAE graph automatic coding neural network, then the key point information and the triangularization vertex are combined into a graph to be input into a GAT graph attention network, and body type information in the SMPL model is obtained through learning.
And A6, re-projecting the obtained SMPL model on each image for alignment, and projecting the color and texture information in the image on the triangulated vertex of the model by using the computer graphics technology to complete texture mapping. And B, importing the SMPL model obtained in the step A5 into a Unity program, aligning all images with the SMPL model in a ring arrangement by combining internal and external parameters calibrated by a camera, re-projecting the SMPL model onto each image to acquire pixel color information of corresponding triangular vertexes, performing Gaussian smoothing noise reduction processing on the pixel information of the vertexes of the overlapped part, and finally completing the texture mapping process of the model.
The multi-view three-dimensional human body reconstruction system based on the graph neural network comprises a camera, a processor and a memory, wherein a program which can be operated by the processor is stored in the memory, and the processor can realize the multi-view three-dimensional human body reconstruction method based on the graph neural network by operating the program.
The method for applying the graph neural network to the human body three-dimensional reconstruction is different from the existing method for obtaining the depth map by using the convolutional neural network, and because the SMPL human body three-dimensional reconstruction model can be well imported into the Unity application program, the Maya application program and the like, the method for deducing the SMPL human body three-dimensional reconstruction model parameters from a plurality of human body images by using the graph neural network can realize more accurate human body three-dimensional reconstruction and can be used for making animations, virtual reality and the like.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it should not be understood that the scope of the present invention is limited thereby. It should be noted that those skilled in the art should recognize that they may make equivalent variations to the embodiments of the present invention without departing from the spirit and scope of the present invention.
Claims (10)
1. A multi-view three-dimensional human body reconstruction method based on a graph neural network is characterized by comprising the following steps:
a2, when a human body is placed in the center of the annular camera, acquiring a human body image;
a3, acquiring a plurality of images, selecting a reference image from the images, and recording the rest images as source images;
a4, constraining each reference image and the source images of two sides of each reference image positioned in the middle of the adjacent reference images by using polar line constraint conditions in multi-view geometry, searching to obtain a plurality of groups of skeleton point coordinates of which the number and the positions accord with the definition of a parameterized human body model, averaging the plurality of groups of skeleton point coordinates to obtain a final group of joint coordinates, extracting characteristic points on the surface of the human body to generate human body shape sparse point clouds serving as surface contour mesh preset vertexes of the parameterized human body model;
a5, selecting partial point clouds around the skeleton points, associating the skeleton points with the selected point clouds to form natural image data, inputting image attention neural network training to obtain the posture information parameters and body type information parameters of a parameterized human body model, and reconstructing a human body three-dimensional model in the output of the parameterized human body model by utilizing output parameters;
and A6, re-projecting the obtained parameterized human body model onto each image, aligning, and projecting color and texture information in the image onto a triangulated vertex of the model by using a computer graphics technology to complete texture mapping.
2. The figure neural network-based multi-view three-dimensional human body reconstruction method according to claim 1, further comprising a step a1 before the step a2, wherein the step a1 specifically comprises erecting a plurality of cameras in a circular arrangement around a human body capture area, and calibrating the plurality of cameras.
3. The method of claim 2, wherein the calibrating the plurality of cameras comprises internally participating in external reference calibration of the cameras by a black and white checkerboard calibration method.
4. The method for multi-view three-dimensional human body reconstruction based on graph neural network as claimed in claim 1, wherein in said step a4, feature points on the surface of human body are extracted by using a scale invariant feature transform algorithm.
5. The method for reconstructing a multi-view three-dimensional human body based on a graph neural network as claimed in claim 1, wherein in said step a5, a part of point cloud around said bone point is extracted by K-nearest neighbor method.
6. The method according to claim 1, wherein in step a5, the neural network is automatically encoded to obtain key point information of pose information, and the key point information and the triangulated vertex are combined into a graph and input to the graph attention neural network to learn the body type information in the parameterized human model.
7. The multi-view three-dimensional human body reconstruction method based on graph neural network as claimed in claim 1, wherein in said step a5, said graph attention neural network is implemented by graph attention layer stack, and the attention calculation coefficient of each graph attention layer is:
wherein alpha isijIs the attention coefficient, N, from the vertex j to the bone point iiAll neighboring vertices, W, representing bone points iijThe weight coefficient between the vertex j and the bone point i, i.e. the L2 norm, is represented, a represents the weight vector, and LeakyReLU represents the LeakyReLU activation function.
8. The method for multi-view three-dimensional human body reconstruction based on graph neural network as claimed in claim 1, wherein in said step a6, color information in the pixels of the video image is extracted by using computer graphics technique and is re-projected onto the skin vertex of the parameterized human body model to complete texture mapping.
9. The method for multi-view three-dimensional human body reconstruction based on graph neural network according to any one of claims 1-8, characterized in that the parameterized human body model is selected as a skinned multi-person linear model, the skinned multi-person linear model comprises two parameters of attitude information θ and body type information β, the attitude information θ is defined by 24 body key points, and the body type information β is 10 parameters for controlling the change of the shape of the human body and represents a triangulated skinned vertex forming the surface of the model.
10. A multi-view three-dimensional human body reconstruction system based on a graph neural network, which comprises a camera, a processor and a memory, wherein the memory stores a program for the processor to run, and the processor can realize the multi-view three-dimensional human body reconstruction method based on the graph neural network according to any one of claims 1 to 9 by running the program.
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