# Human Motion Capture Based on Incremental Dimension Reduction and Projection Position Optimization

Authors: Wanyi Li; Yuqi Zeng; Qian Zhang; Yilin Wu; Guoming Chen
Journal: Wireless Communications and Mobile Computing
 (2021)

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## Abstract

Three-dimensional (3D) human motion capture is a hot researching topic at present. The network becomes advanced nowadays, the appearance of 3D human motion is indispensable in the multimedia works, such as image, video, and game. 3D human motion plays an important role in the publication and expression of all kinds of medium. How to capture the 3D human motion is the key technology of multimedia product. Therefore, a new algorithm called incremental dimension reduction and projection position optimization (IDRPPO) is proposed in this paper. This algorithm can help to learn sparse 3D human motion samples and generate the new ones. Thus, it can provide the technique for making 3D character animation. By taking advantage of the Gaussian incremental dimension reduction model (GIDRM) and projection position optimization, the proposed algorithm can learn the existing samples and establish the relevant mapping between the low dimensional (LD) data and the high dimensional (HD) data. Finally, the missing frames of input 3D human motion and the other type of 3D human motion can be generated by the IDRPPO.


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## Body

## 1. Introduction

Three-dimensional (3D) human motion capture is applied for many fields, such as medical diagnosis, animation making, and 3D video game development [1–3]. How to generate the human motion in 3D becomes curial to these works. Human motion in 3D is depicted by high-dimensional (HD) data, and the motion sequence consists of poses. Each pose can be exhibited by a human motion model. One complete motion cycle is called a gait.3D human motion capture has been developed into a hot researching topic. How to generate the human motion in 3D has various techniques. One of the hot techniques is the reconstruction of 3D human motion from the image sequence, which needs some complex preprocessing methods to extract the image feature and analyze feature sample, such as video event analysis [4] and video feature analysis [5]. Another one is 3D human motion estimation of self-supervised learning, which is learning the sparse samples of one type human motion and generating the other type human motion. Then, the method of self-supervised learning will be mainly discussed in this article. The self-supervised learning can be seen as the special case of unsupervised learning to some extent. Recently, there are some methods which contain defects. In [6, 7], some heuristic algorithms are used to process the preprocessed image for generating the human motion. It will cost too much time, and the quality of generated human motion is susceptible to preprocessing quality of the image. The accuracy and efficiency are low. Some dimension reduction models [8–11] can process the human motion efficiently, but the HD data of the human motion can only be visualized by these models in low-dimensional (LD) space. Some improved dimension reduction models [12] have two mappings between LD space and HD space, which can generate the LD data sample for transformation of the HD data sample. These models will do great help to generate human motion, but the other type of human motion cannot be obtained. A certain improved method in [13] is proposed to fit the human motion sequence, which needs to process the LD data in LD space and increases the difficulty of generating the human motion. These methods above also cannot fast obtain one type motion from the other type directly. In summary, how to generate one type motion from the other type directly is not an easy task in a short time. The CNN [14] and its corresponding networks are emerging in the recent years (e.g., Resnet [15], AlexNet [16], VGG [17], SqueezeNet [18], DenseNet [19], and Inception [20]), but these networks working will need much training time, a large amount of datasets, and huge budget of hardware, which will even need the high-level and costly GPU for work. Thus, a new machine learning method need to be proposed, which is suitable for fast making the animation of 3D human character. Meanwhile, the proposed method can generate the new valid train data and corresponding pseudolabel (self-encoded) data (LD data), which can be used to retrain the model and improve the prediction. In general, it can improve the self-supervised learning model. The data sequence seen as a matrix can be processed by the proposed method directly, so that it can promote performance of some frameworks of tracking and estimation to a certain extent, such as self-supervised seep correlation tracking [21] (self-SDCT). Without the artificial annotation, the proposed method can obtain the new essential samples according to the data requirement of the self-supervised learning model and let the model update the generating mapping for the improvement of tracking or estimating by the help of these samples.In this paper, the new algorithm (method) called incremental dimension reduction and projection position optimization (IDRPPO) is proposed to address the problems mentioned above. It can generate one type human motion from the other type. In addition, the input motion samples can be incomplete gait. IDRPPO will show the promising performance from the experimental tests of visual effect and error. IDRPPO will take advantage of Gaussian incremental dimension reduction model (GIDRM) [7] and projection position optimization to carry out the self-supervised learning of small-scale samples. GIDRM is similar to the bilinear analysis model of compound rank-k projections [22](CRP). Inspired by CRP, the adoption of GIDRM can process the complex HD data of the 3D human motion and make these HD data visualized and regularized. Firstly, GIDRM can process the matrix directly without the transformation of the vectors, which is conducive to decrease the computation complexity and improve the model flexibility. The matrix can denote the HD sample sequence of human motion or the corresponding LD data sequence. Secondly, GIDRM can provide the LD space for searching and generating the optimal LD data sample, so that the corresponding 3D human motion can be reconstructed by its mappings. The two advantages are essential to the efficiency of IDRPPO for estimating the 3D human motion. Thus, IDRPPO with the GIDRM can learn one type incomplete gait, then the missing frames in incomplete gait and the other type motion can be output perfectly by it. Our contributions are listed as follows:
(1)
Address the problem of filling the missing frames in the incomplete motion cycle and make the motion cycle complete and smooth(2)
Address the problem of generating the other type motion cycle from the origin incomplete motion cycle by the help of the IDRPPOThe performance of the IDRPPO will be tested from the experiments, and the results will indicate the IDRPPO can help to achieve the promising visual effect and low estimating error for human motion capture. The technique framework of IDRPPO can be seen in Figure1. Then, the details of IDRPPO will be discussed in the following sections.Figure 1
The technique framework of IDRPPO.


## 2. Generation of Human Motion through IDRPPO

### 2.1. Gaussian Incremental Dimension Reduction Model

According to the references above, the models can be given as follows [7, 12]:
(1)pY∣X,β¯,W=WN2πNDKYDexp−12trKY−1YW2YT,(2)pXα¯=px12πN−1qKxqexp−12trKX−1X2:NX2:NT.From Equation (2) and Equation (1), HD data sequence can be denoted by Y=y1,⋯,yi,⋯,yNT∈RN×D, yi∈RD. LD data sequence can be denoted by X=x1,⋯,xi,⋯,xNT∈RN×q, xi∈Rq. Kernel matrix is denoted by KY∈RN×N,KYi,j=kYxi,xj=exp−β1/2xi−xj2+β2−1δxixj. β¯=β1,β2 are the kernel parameters of KY, and the other kernel matrix can be denoted by KX∈RN−1×N−1, KXi,j=kXxi,xj=α1exp−α2/2xi−xj2+α3xiTxj+α4−1δxixj. α¯=α1,α2,α3,α4 are the kernel parameters of KX. W is the scale parameter matrix, then pW=∏m=1D2/κ2πexp−wm2/2κ2, wm>0,κ=10−3. Let X2:N=x2,x3,..,xNT, and x1 confronts the of q-dimensional Gaussian distribution. α¯ and β¯ satisfy pα¯∝∏iαi−1 and pβ¯∝∏iβi−1, respectively. In Equation (1) and Equation (2), Y is known; thus, pY is constant, and the equivalence of min−lnpX,α¯,β¯,W∣Y⇔min−lnpX,α¯,β¯,W,Y can be got. The LD data and corresponding parameters can be obtained as follows:
(3)X,α¯,β¯,W=argminX,α¯,β¯,W−lnpX,α¯,β¯,W,Y=argminX,α¯,β¯,W−lnpY∣X,β¯,WpX∣α¯pα¯pβ¯pW,where y~NμYx,σY2xI, y∈RD,x∈Rq, the mapping from HD space to LD space can be built as follows:
(4)y=fx=μYx=YTKY−1kYx1,x,kYx2,x,⋯,kYxN,xT=YTKY−1kYx.If two or more mappings from LD space to HD space need to be built, Equation (3) can be retrained according to the needs. After building the first mapping, the LD data from the first mapping can be fixed, which can be seen as the initial LD data of the second mapping training.Then, the mapping of the incremental dimension reduction is built as follows:(5)X=ΦWD,where Φ∈RN×Nk is radial basis function, Φk1,k2=ϕyk1,ck2=expbyk1−ck22. WD∈RNk×q is the weight matrix, Nk≤N. W⌢D is least squares estimator, W⌢D=ΦTΦ−1ΦTX. Then, y∗∈RD denotes the new HD data sample, x∗∈RD denotes the LD data of y∗∈RD. If b is known, the mapping from y∗ to x∗ can be given as follows:
(6)x∗=gy∗=Φy∗W⌢DT=W⌢DTΦy∗T,where Φy∗=ϕy∗,c1,ϕy∗,c2,⋯,ϕy∗,cNk, then we can get the equation as follows:
(7)X=ΦW⌢D+e.In Equation (7), e∈RN×Nk is the error matrix, let e=e1,⋯,ek1,⋯,eNT=e1′,⋯,ev1′,⋯,eq′∈RN×q. Then, X=x1,⋯,xk1,⋯,xNT=x1′,⋯,xv1′,⋯,xq′∈RN×q, W⌢D=w⌢1,⋯,w⌢k2,⋯,w⌢NkT=w⌢1′,⋯,w⌢v1′,⋯,w⌢q′∈RNk×q, and Φ=φ1,⋯,φk2,⋯,φNk∈RN×Nk. Let Φ=W~A~, and W~=w~1,⋯,w~k2,⋯,w~Nk∈RN×Nk is a diagonal matrix (w~k2Tw~k2′=0,k2≠k2′;w~k2Tw~k2′≠0,k2=k2′). A~∈RNk×Nk is an invertible matrix (A~i,j=ai,j,i<j;A~i,j=1,i=j;A~i,j=0,i>j). We have W⌢D=ΦTΦ−1ΦTX, then let g⌢=W~TW~−1W~TX and g⌢=g1,⋯,gk2,⋯,gNkT=g⌢1,⋯,g⌢v1,⋯,g⌢q∈RNk×q. The equation can be got as follows:
(8)ΦW⌢D=ΦΦTΦ−1ΦTX=W~A~A~TW~TW~A~−1A~TW~TX=W~W~TW~−1W~TX=W~g⌢.Thus, Equation (8) can be written as:
(9)X=W~g⌢+e.According to the properties of least squares, W~Te=0,eTW~=0, we have:
(10)XTX=W~g⌢+eTW~g⌢+e=g⌢TW~TW~g⌢+g⌢TW~Te+eTW~g⌢+eTe=g⌢TW~TW~g⌢+eTe,where eTe=XTX−g⌢TW~TW~g⌢. When training, the Nk orthogonal vectors can be replaced; the equation can be got as follows:
(11)minX−ΦW⌢D2/N×q=minX−W~g⌢2/N×q=mintreTe/N×q=min1q∑v1=1q1Nev1′Tev1′=min1q∑v1=1q1Nxv1′Txv1′−1N∑k2=1Nkw~k2Tw~k2g⌢v12k2.Equation (11) is equivalent to the equation as follows:
(12)Sw=argmaxSw∑v1=1q∑k2=1Nkw~k2Tw~k2g⌢v12k2/xv1′Txv1′,Sw∈Sw′,Nk≤N.In Equation (12), Sw=w~1,⋯,w~k2,⋯,w~Nk and Sw′=w~1′,⋯,w~k2′,⋯,w~N′ both are the sets of orthogonal vectors. Sw is the subset of Sw′, Sw′ is the set containing φk2′ which is the vector from Φ′=φ1′,⋯,φk2′,⋯,φN′∈RN×N, then Φk1,k2′=ϕyk1,yk2=expbyk1−yk22. When the tolerance X−ΦW⌢D2/N×q<ε1,ε1>0 is satisfied, the training can be finished. It means that the vector φk2′ is selected as few as possible to minimize the variable NK for the satisfaction of the tolerance, so that the mapping training can be finished.


### 2.2. Projection Position Optimization

The learning of the incomplete gait of human motion needs projection position optimization in the LD space. Let us give some definitions:PrjAB⇀ denotes the projected operation of vector AB⇀, A is the first known LD data before the missing human motion sequence, B is the last known LD data after the missing human motion sequence, and Ci,i=1,2,⋯Nmiss denotes the LD data of the missing frames. According to Figure 2, we have:
(13)PrjAB→AC→−iAB→Nmiss+1=0,(14)C′C→−cNmiss+1=0.After dimension reduction, c in Equation (14) is a preset parameter which denotes the distance between the missing dot and projection dot in Figure 2. The position of missing frames should satisfy Equation (13) and Equation (14); thus, Equation (3) can be trained optimally during the second training. Then, according to Equation (13) and Equation (14), the objective function and gradient function can be got respectively, as follows:
(15)minFXmiss=∑i=1NmissPrjAB→xi−AT−iAB→Nmiss+12+∑i=1Nmissxi−PrjAB→xi−ATAB→/AB→−cNmiss+12,(16)dFXmiss/dxiT=2PrjAB→xi−AT−iAB→/Nmiss+1AB→+2xi−PrjAB→xi−ATAB→/AB→−c/Nmiss+1,1/xi−PrjAB→xi−ATAB→/AB→xi−ATAB→TAB→/AB→2,Iq×q−ABR•ABRT.From Equation (16), ABR=AB→TAB→TAB→TT, “•” denotes product of the entry of matrix. The solution of Equation (15) will not be a unique solution, but any of the solutions can keep the relative position of each missing frame in the LD space during training. Thus, the second training can obtain the LD data samples of missing frames. The solution of Equation (15) can be got by some traditional gradient optimization methods [23].Figure 2
The framework of projection position optimization.


### 2.3. The Procedure of Generating the Human Motion

Some definitions are listed as follows:YI and YII are denoted as HD data sample sequences of type I and II human motions, respectively; YI contains the missing frames; X1 and X2 are denoted as the LD data sequences of YI and YII, respectively; yI′ and yII′ are denoted as the new HD samples of type I and II human motions, respectively. Then, the procedure of generating the human motion is summarized as follows:
(1)
Equation (3) can be used to process the YI which is containing missing frames for dimension reduction; then, X1 and corresponding training parameters can be obtained (the external and internal iteration numbers of this step are set to S11 and S12, respectively)(2)
Adopt the projection position optimization to processX1. It is equivalent to minimize Equation (15) by the help of Equation (16) (the iteration number of this step is set to S21)(3)
The training parameters in step 1 andX1 processed in step 2 can be took into Equation (3) for the second training, then the training parameters, the updated X1 and mapping f1 from X1 to YI can be obtained. The missing frames in the YI can be generated from X1 processed in step 2. Build the mapping g from YI to X1 through Equation (5) next (the external and internal iteration numbers of building f1 are set to S31 and S32, respectively, the iteration numbers of building g is set to NkNk≤N)(4)
Build the mappingf2 from X1 to YII through Equation (3), X1 is obtained from step 3, and X1 is fixed during this training. After finishing the training of Equation (3), the mapping f2 can be obtained (the external and internal iteration numbers of building f2 are set to S41 and S42, respectively)(5)
When there comesyI’, yII’ can be generated by the equation yII′=f2gyI′.The computational complexity of the whole algorithm is depending on the iteration number of each step usually. The computational complexity is denoted by O., which is mainly described by the time frequency. If the data preprocessing and matrix calculation are without consideration, as the result of which are not the core steps of proposed algorithm, we can get the computational complexity is OS11S12+S21+S31S32+Nk+S41S42. Thus, the computational complexity is depending on each iteration number which can reach the max iterative magnitude.


## 2.1. Gaussian Incremental Dimension Reduction Model

According to the references above, the models can be given as follows [7, 12]:
(1)pY∣X,β¯,W=WN2πNDKYDexp−12trKY−1YW2YT,(2)pXα¯=px12πN−1qKxqexp−12trKX−1X2:NX2:NT.From Equation (2) and Equation (1), HD data sequence can be denoted by Y=y1,⋯,yi,⋯,yNT∈RN×D, yi∈RD. LD data sequence can be denoted by X=x1,⋯,xi,⋯,xNT∈RN×q, xi∈Rq. Kernel matrix is denoted by KY∈RN×N,KYi,j=kYxi,xj=exp−β1/2xi−xj2+β2−1δxixj. β¯=β1,β2 are the kernel parameters of KY, and the other kernel matrix can be denoted by KX∈RN−1×N−1, KXi,j=kXxi,xj=α1exp−α2/2xi−xj2+α3xiTxj+α4−1δxixj. α¯=α1,α2,α3,α4 are the kernel parameters of KX. W is the scale parameter matrix, then pW=∏m=1D2/κ2πexp−wm2/2κ2, wm>0,κ=10−3. Let X2:N=x2,x3,..,xNT, and x1 confronts the of q-dimensional Gaussian distribution. α¯ and β¯ satisfy pα¯∝∏iαi−1 and pβ¯∝∏iβi−1, respectively. In Equation (1) and Equation (2), Y is known; thus, pY is constant, and the equivalence of min−lnpX,α¯,β¯,W∣Y⇔min−lnpX,α¯,β¯,W,Y can be got. The LD data and corresponding parameters can be obtained as follows:
(3)X,α¯,β¯,W=argminX,α¯,β¯,W−lnpX,α¯,β¯,W,Y=argminX,α¯,β¯,W−lnpY∣X,β¯,WpX∣α¯pα¯pβ¯pW,where y~NμYx,σY2xI, y∈RD,x∈Rq, the mapping from HD space to LD space can be built as follows:
(4)y=fx=μYx=YTKY−1kYx1,x,kYx2,x,⋯,kYxN,xT=YTKY−1kYx.If two or more mappings from LD space to HD space need to be built, Equation (3) can be retrained according to the needs. After building the first mapping, the LD data from the first mapping can be fixed, which can be seen as the initial LD data of the second mapping training.Then, the mapping of the incremental dimension reduction is built as follows:(5)X=ΦWD,where Φ∈RN×Nk is radial basis function, Φk1,k2=ϕyk1,ck2=expbyk1−ck22. WD∈RNk×q is the weight matrix, Nk≤N. W⌢D is least squares estimator, W⌢D=ΦTΦ−1ΦTX. Then, y∗∈RD denotes the new HD data sample, x∗∈RD denotes the LD data of y∗∈RD. If b is known, the mapping from y∗ to x∗ can be given as follows:
(6)x∗=gy∗=Φy∗W⌢DT=W⌢DTΦy∗T,where Φy∗=ϕy∗,c1,ϕy∗,c2,⋯,ϕy∗,cNk, then we can get the equation as follows:
(7)X=ΦW⌢D+e.In Equation (7), e∈RN×Nk is the error matrix, let e=e1,⋯,ek1,⋯,eNT=e1′,⋯,ev1′,⋯,eq′∈RN×q. Then, X=x1,⋯,xk1,⋯,xNT=x1′,⋯,xv1′,⋯,xq′∈RN×q, W⌢D=w⌢1,⋯,w⌢k2,⋯,w⌢NkT=w⌢1′,⋯,w⌢v1′,⋯,w⌢q′∈RNk×q, and Φ=φ1,⋯,φk2,⋯,φNk∈RN×Nk. Let Φ=W~A~, and W~=w~1,⋯,w~k2,⋯,w~Nk∈RN×Nk is a diagonal matrix (w~k2Tw~k2′=0,k2≠k2′;w~k2Tw~k2′≠0,k2=k2′). A~∈RNk×Nk is an invertible matrix (A~i,j=ai,j,i<j;A~i,j=1,i=j;A~i,j=0,i>j). We have W⌢D=ΦTΦ−1ΦTX, then let g⌢=W~TW~−1W~TX and g⌢=g1,⋯,gk2,⋯,gNkT=g⌢1,⋯,g⌢v1,⋯,g⌢q∈RNk×q. The equation can be got as follows:
(8)ΦW⌢D=ΦΦTΦ−1ΦTX=W~A~A~TW~TW~A~−1A~TW~TX=W~W~TW~−1W~TX=W~g⌢.Thus, Equation (8) can be written as:
(9)X=W~g⌢+e.According to the properties of least squares, W~Te=0,eTW~=0, we have:
(10)XTX=W~g⌢+eTW~g⌢+e=g⌢TW~TW~g⌢+g⌢TW~Te+eTW~g⌢+eTe=g⌢TW~TW~g⌢+eTe,where eTe=XTX−g⌢TW~TW~g⌢. When training, the Nk orthogonal vectors can be replaced; the equation can be got as follows:
(11)minX−ΦW⌢D2/N×q=minX−W~g⌢2/N×q=mintreTe/N×q=min1q∑v1=1q1Nev1′Tev1′=min1q∑v1=1q1Nxv1′Txv1′−1N∑k2=1Nkw~k2Tw~k2g⌢v12k2.Equation (11) is equivalent to the equation as follows:
(12)Sw=argmaxSw∑v1=1q∑k2=1Nkw~k2Tw~k2g⌢v12k2/xv1′Txv1′,Sw∈Sw′,Nk≤N.In Equation (12), Sw=w~1,⋯,w~k2,⋯,w~Nk and Sw′=w~1′,⋯,w~k2′,⋯,w~N′ both are the sets of orthogonal vectors. Sw is the subset of Sw′, Sw′ is the set containing φk2′ which is the vector from Φ′=φ1′,⋯,φk2′,⋯,φN′∈RN×N, then Φk1,k2′=ϕyk1,yk2=expbyk1−yk22. When the tolerance X−ΦW⌢D2/N×q<ε1,ε1>0 is satisfied, the training can be finished. It means that the vector φk2′ is selected as few as possible to minimize the variable NK for the satisfaction of the tolerance, so that the mapping training can be finished.


## 2.2. Projection Position Optimization

The learning of the incomplete gait of human motion needs projection position optimization in the LD space. Let us give some definitions:PrjAB⇀ denotes the projected operation of vector AB⇀, A is the first known LD data before the missing human motion sequence, B is the last known LD data after the missing human motion sequence, and Ci,i=1,2,⋯Nmiss denotes the LD data of the missing frames. According to Figure 2, we have:
(13)PrjAB→AC→−iAB→Nmiss+1=0,(14)C′C→−cNmiss+1=0.After dimension reduction, c in Equation (14) is a preset parameter which denotes the distance between the missing dot and projection dot in Figure 2. The position of missing frames should satisfy Equation (13) and Equation (14); thus, Equation (3) can be trained optimally during the second training. Then, according to Equation (13) and Equation (14), the objective function and gradient function can be got respectively, as follows:
(15)minFXmiss=∑i=1NmissPrjAB→xi−AT−iAB→Nmiss+12+∑i=1Nmissxi−PrjAB→xi−ATAB→/AB→−cNmiss+12,(16)dFXmiss/dxiT=2PrjAB→xi−AT−iAB→/Nmiss+1AB→+2xi−PrjAB→xi−ATAB→/AB→−c/Nmiss+1,1/xi−PrjAB→xi−ATAB→/AB→xi−ATAB→TAB→/AB→2,Iq×q−ABR•ABRT.From Equation (16), ABR=AB→TAB→TAB→TT, “•” denotes product of the entry of matrix. The solution of Equation (15) will not be a unique solution, but any of the solutions can keep the relative position of each missing frame in the LD space during training. Thus, the second training can obtain the LD data samples of missing frames. The solution of Equation (15) can be got by some traditional gradient optimization methods [23].Figure 2
The framework of projection position optimization.


## 2.3. The Procedure of Generating the Human Motion

Some definitions are listed as follows:YI and YII are denoted as HD data sample sequences of type I and II human motions, respectively; YI contains the missing frames; X1 and X2 are denoted as the LD data sequences of YI and YII, respectively; yI′ and yII′ are denoted as the new HD samples of type I and II human motions, respectively. Then, the procedure of generating the human motion is summarized as follows:
(1)
Equation (3) can be used to process the YI which is containing missing frames for dimension reduction; then, X1 and corresponding training parameters can be obtained (the external and internal iteration numbers of this step are set to S11 and S12, respectively)(2)
Adopt the projection position optimization to processX1. It is equivalent to minimize Equation (15) by the help of Equation (16) (the iteration number of this step is set to S21)(3)
The training parameters in step 1 andX1 processed in step 2 can be took into Equation (3) for the second training, then the training parameters, the updated X1 and mapping f1 from X1 to YI can be obtained. The missing frames in the YI can be generated from X1 processed in step 2. Build the mapping g from YI to X1 through Equation (5) next (the external and internal iteration numbers of building f1 are set to S31 and S32, respectively, the iteration numbers of building g is set to NkNk≤N)(4)
Build the mappingf2 from X1 to YII through Equation (3), X1 is obtained from step 3, and X1 is fixed during this training. After finishing the training of Equation (3), the mapping f2 can be obtained (the external and internal iteration numbers of building f2 are set to S41 and S42, respectively)(5)
When there comesyI’, yII’ can be generated by the equation yII′=f2gyI′.The computational complexity of the whole algorithm is depending on the iteration number of each step usually. The computational complexity is denoted by O., which is mainly described by the time frequency. If the data preprocessing and matrix calculation are without consideration, as the result of which are not the core steps of proposed algorithm, we can get the computational complexity is OS11S12+S21+S31S32+Nk+S41S42. Thus, the computational complexity is depending on each iteration number which can reach the max iterative magnitude.


## 3. Experiment and Evaluation

Some heuristic algorithms and dimension reduction models cannot generate one type human motion from the other type mostly. How to optimize the projection position is the key to the generation of human motion. Thus, the algorithm using incremental dimension reduction with no projection position optimization can be called IDRNPPO. IDRNPPO and IDRPPO will be used to generate the human motion for the experimental tests. In the experiments, the visual effect and error from the missing frames and generated poses will be the evaluation criterion of the performance. The missing frames can adopt the walking motion, and the generated motion can adopt running motion which will be generated by the walking motion. Our test environment is listed as follows:CPU: i7-9750HRAM: 16GBGPU: Nvidia GTX 1660Ti 6GBHD: 1.5TB solid state diskSoftware: MATLAB R2009b


### 3.1. The Visual Comparison

IDRPPO and IDRNPPO are used to generate the human running motion, respectively, when the input incomplete motion is walking. The results can be seen in Figures3 and 4.Figure 3
The visual comparison of generating the human running motion between IDRPPO and IDRNPPO.
(a)
The samples of the input walking motion(b)
The samples of the output human running motion from IDRNPPO(c)
The samples of the output human running motion from IDRPPOFigure 4
The visual comparison of generating the walking missing frames between IDRPPO and IDRNPPO.
(a)
The samples of generating the walking missing frames from IDRNPPO(b)
The samples of generating the walking missing frames from IDRPPOFrom Figure3, the human running poses from IDRPPO are better than the ones from IDRNPPO in the visual effect. The 30th, 35th, 40th, 45th, 48th, 52nd, and 58th frames from the IDRNPPO are the same, which cannot constitute the smooth motion sequence to show the running process. Furthermore, from Figure 4, the missing frames in the input motion from IDRNPPO are also the same, which cannot display the missing smooth walking sequence. However, the running motion and the missing walking motion from the IDRPPO are very smooth, which are constituting the ideal sequences of running motion and the missing walking motion, respectively. The running time testing results are reported in Table 1. From Table 1, when generating the running motion, the IDRNPPO consumes 7.83 seconds, and the IDRPPO consumes 7.96 seconds; thus, the running times of both are close. Then, when generating the missing walking motion, the running times of both are also close, the IDRNPPO is 2.15 seconds, and the IDRPPO is 2.13 seconds. From the running time test, it can be found that the IDRPPO will not be time-consuming relatively. In Figure 5, the LD data of missing frames from IDRNPPO and IDRPPO are obviously different, which are denoted by the green ones in Figure 5(a) and Figure 5(b), respectively. The ones of IDRNPPO are without projection position optimization. They are becoming a mess carve, which are difficult to be distinguished. On the contrary, the ones of IDRPPO are very neat and smooth, which can constitute the missing part from the whole curve. The results of Figure 5 can also explain why the missing frames of IDRPPO will be the smooth motion sequence in another aspect. On the whole, Figures 3, 4, and 5 can indicate IDRPPO has better performance than IDRNPPO.Table 1
The running time test (seconds).
Test typeTest contentIDRNPPOIDRPPOVisual testGenerating running motion7.837.96Generating missing walking motion2.152.13Error testGenerating running motion and missing walking motion8.289.51Figure 5
The LD data of the missing frames in LD space.
(a)
The LD data of the missing frames from IDRNPPO (green ones)(b)
The LD data of the missing frames from IDRPPO (green ones)


### 3.2. The Error of the Generation

The IDRPPO and IDRNPPO can be seen in Figure6, respectively. How to calculate error can be seen in [24]. From Figure 6, the errors of the human running motion and the missing walking motion from IDRPPO are lower than IDRNPPO on the whole. It is the normal phenomenon that some frames of both have the close error in Figure 6(a), because some frames of IDRNPPO can display the running motion correctly. However, the tendency of errors can be evaluated by mean error. The mean error from IDRPPO is lower than IDRNPPO as depicted in Figure 6(b). From Table 1, it can be found that the runtime testing results are 8.28 seconds (IDRNPPO) and 9.51 seconds (IDRPPO), respectively. The small gap of the required running times for both will also be indicated. Finally, the results of Figure 6 can illustrate the IDRPPO performance of generating the motion is better than the IDRNPPO again.Figure 6
The error comparison of IDRPPO and IDRNPPO.
(a)
The error of generating running motion(b)
The error of generating the missing walking motion


## 3.1. The Visual Comparison

IDRPPO and IDRNPPO are used to generate the human running motion, respectively, when the input incomplete motion is walking. The results can be seen in Figures3 and 4.Figure 3
The visual comparison of generating the human running motion between IDRPPO and IDRNPPO.
(a)
The samples of the input walking motion(b)
The samples of the output human running motion from IDRNPPO(c)
The samples of the output human running motion from IDRPPOFigure 4
The visual comparison of generating the walking missing frames between IDRPPO and IDRNPPO.
(a)
The samples of generating the walking missing frames from IDRNPPO(b)
The samples of generating the walking missing frames from IDRPPOFrom Figure3, the human running poses from IDRPPO are better than the ones from IDRNPPO in the visual effect. The 30th, 35th, 40th, 45th, 48th, 52nd, and 58th frames from the IDRNPPO are the same, which cannot constitute the smooth motion sequence to show the running process. Furthermore, from Figure 4, the missing frames in the input motion from IDRNPPO are also the same, which cannot display the missing smooth walking sequence. However, the running motion and the missing walking motion from the IDRPPO are very smooth, which are constituting the ideal sequences of running motion and the missing walking motion, respectively. The running time testing results are reported in Table 1. From Table 1, when generating the running motion, the IDRNPPO consumes 7.83 seconds, and the IDRPPO consumes 7.96 seconds; thus, the running times of both are close. Then, when generating the missing walking motion, the running times of both are also close, the IDRNPPO is 2.15 seconds, and the IDRPPO is 2.13 seconds. From the running time test, it can be found that the IDRPPO will not be time-consuming relatively. In Figure 5, the LD data of missing frames from IDRNPPO and IDRPPO are obviously different, which are denoted by the green ones in Figure 5(a) and Figure 5(b), respectively. The ones of IDRNPPO are without projection position optimization. They are becoming a mess carve, which are difficult to be distinguished. On the contrary, the ones of IDRPPO are very neat and smooth, which can constitute the missing part from the whole curve. The results of Figure 5 can also explain why the missing frames of IDRPPO will be the smooth motion sequence in another aspect. On the whole, Figures 3, 4, and 5 can indicate IDRPPO has better performance than IDRNPPO.Table 1
The running time test (seconds).
Test typeTest contentIDRNPPOIDRPPOVisual testGenerating running motion7.837.96Generating missing walking motion2.152.13Error testGenerating running motion and missing walking motion8.289.51Figure 5
The LD data of the missing frames in LD space.
(a)
The LD data of the missing frames from IDRNPPO (green ones)(b)
The LD data of the missing frames from IDRPPO (green ones)


## 3.2. The Error of the Generation

The IDRPPO and IDRNPPO can be seen in Figure6, respectively. How to calculate error can be seen in [24]. From Figure 6, the errors of the human running motion and the missing walking motion from IDRPPO are lower than IDRNPPO on the whole. It is the normal phenomenon that some frames of both have the close error in Figure 6(a), because some frames of IDRNPPO can display the running motion correctly. However, the tendency of errors can be evaluated by mean error. The mean error from IDRPPO is lower than IDRNPPO as depicted in Figure 6(b). From Table 1, it can be found that the runtime testing results are 8.28 seconds (IDRNPPO) and 9.51 seconds (IDRPPO), respectively. The small gap of the required running times for both will also be indicated. Finally, the results of Figure 6 can illustrate the IDRPPO performance of generating the motion is better than the IDRNPPO again.Figure 6
The error comparison of IDRPPO and IDRNPPO.
(a)
The error of generating running motion(b)
The error of generating the missing walking motion


## 4. Conclusion

The IDRPPO is proposed to obtain the 3D human motion. IDRPPO with the GIDRM can help to learn the incomplete gait, and generate the other gait, which makes up the defects of some self-supervised or unsupervised algorithms. From the experiments, the projection position is crucial to the performance of IDRPPO. The experimental results can reveal IDRPPO is efficacious in making 3D human character animation, which can do great help to generating the motion cycle fast. IDRPPO can promote the small-scale self-supervised or unsupervised learning undoubtedly. However, IDRPPO cannot process the complex and irregular human motion samples, which will be improved in the future research. The human motion model can be replaced by a more advantaged model [25], so that the high-level multimedia product can be made by this technique.



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*Source: 5589100-2021-04-29.xml*