In the proposed paper,a parallel structure type Generative Adversarial Network(GAN)using edge and texture information is proposed.In the existing GAN-based model,many learning iterations had to be given to obtaining a...In the proposed paper,a parallel structure type Generative Adversarial Network(GAN)using edge and texture information is proposed.In the existing GAN-based model,many learning iterations had to be given to obtaining an output that was somewhat close to the original data,and noise and distortion occurred in the output image even when learning was performed.To solve this problem,the proposed model consists of two generators and three discriminators to propose a network in the form of a parallel structure.In the network,each edge information and texture information were received as inputs,learning was performed,and each character was combined and outputted through the Combine Discriminator.Through this,edge information and distortion of the output image were improved even with fewer iterations than DCGAN,which is the existing GAN-based model.As a result of learning on the network of the proposed model,a clear image with improved contour and distortion of objects in the image was output from about 50,000 iterations.展开更多
To address the problems of lack of high-frequency information and texture details and unstable training in superresolution generative adversarial net-works,this paper optimizes the generator and discriminator based on...To address the problems of lack of high-frequency information and texture details and unstable training in superresolution generative adversarial net-works,this paper optimizes the generator and discriminator based on the SRGAN model.First,the residual dense block is used as the basic structural unit of the gen-erator to improve the network’s feature extraction capability.Second,enhanced lightweight coordinate attention is incorporated to help the network more precisely concentrate on high-frequency location information,thereby allowing the gener-ator to produce more realistic image reconstruction results.Then,we propose a symmetric and efficient pyramidal segmentation attention discriminator network in which the attention mechanism is capable of derivingfiner-grained multiscale spatial information and creating long-term dependencies between multiscale chan-nel attentions,thus enhancing the discriminative ability of the network.Finally,a Charbonnier loss function and a gradient variance loss function with improved robustness are used to better realize the image’s texture structure and enhance the model’s stability.Thefindings from the experiments reveal that the reconstructed image quality enhances the average peak signal-to-noise ratio(PSNR)by 1.59 dB and the structural similarity index(SSIM)by 0.045 when compared to SRGAN on the three test sets.Compared with the state-of-the-art methods,the reconstructed images have a clearer texture structure,richer high-frequency details,and better visual effects.展开更多
基于图像的物体三维重建一直是计算机视觉领域的研究热点.与二维人脸图像相比,三维人脸模型能够承载更多的信息从而具有更广泛的应用前景,如更精准的身份信息识别标志、更准确的情感表达媒介等.为了从单幅大视角的二维人脸图像中重建出...基于图像的物体三维重建一直是计算机视觉领域的研究热点.与二维人脸图像相比,三维人脸模型能够承载更多的信息从而具有更广泛的应用前景,如更精准的身份信息识别标志、更准确的情感表达媒介等.为了从单幅大视角的二维人脸图像中重建出具有真实感的三维彩色人脸模型,提出一种结构简单但有效的算法.首先设计一个编码-解码网络,从原始RGB图像生成并记录完整的三维人脸信息的二维UV位置图;然后使用一个卷积神经网络从中重塑出三维人脸;最后考虑人脸大视角时的自遮挡情况,进一步通过条件生成对抗网络补全UV纹理图的缺失.使用Stirling/ESRC 3D Face Database与其他三维人脸重建的算法进行对比实验,结果表明,所提算法能够实现更高的重建精度,特别是在大视角人脸图像重建应用中,即使在复杂环境下也可以获得完整和真实的三维人脸模型.展开更多
针对3D人脸重建方法在贴图时忽视对纹理处理的设计,仅进行仿射变换和插值,其中仿射变换会导致其生成图像的高频分量遭到损坏,尤其是给出嘴部姿态不同的源、目标人像时,会造成人像的嘴部纹理缺失,而插值方法会造成灰度不连续现象;提出一...针对3D人脸重建方法在贴图时忽视对纹理处理的设计,仅进行仿射变换和插值,其中仿射变换会导致其生成图像的高频分量遭到损坏,尤其是给出嘴部姿态不同的源、目标人像时,会造成人像的嘴部纹理缺失,而插值方法会造成灰度不连续现象;提出一种增强的3D人脸替换方法,称为基于生成-重建的人脸替换(generative reconstructed face swap,GRFS)。GRFS将对抗生成网络应用于对3D人脸替换结果的纹理修复,包括两个子网络:嘴部修复网络(mouth restoration network,MRN)以及局部修复生成网络(generative local restoration network,GLRN)。MRN用于修复人像的嘴部细节,GLRN用于修复3D人脸重建过程中损坏的高频分量,并使得异常的不连续灰度变得光滑。实验结果表明,GRFS可以在给定单对源、目标人像的情况下生成逼真的人脸替换结果,且在不同实验环境下的表现好于主流人脸替换算法。展开更多
As image generation techniques mature,there is a growing interest in explainable representations that are easy to understand and intuitive to manipulate.In this work,we turn to co-occurrence statistics,which have long...As image generation techniques mature,there is a growing interest in explainable representations that are easy to understand and intuitive to manipulate.In this work,we turn to co-occurrence statistics,which have long been used for texture analysis,to learn a controllable texture synthesis model.We propose a fully convolutional generative adversarial network,conditioned locally on co-occurrence statistics,to generate arbitrarily large images while having local,interpretable control over texture appearance.To encourage fidelity to the input condition,we introduce a novel differentiable co-occurrence loss that is integrated seamlessly into our framework in an end-to-end fashion.We demonstrate that our solution offers a stable,intuitive,and interpretable latent representation for texture synthesis,which can be used to generate smooth texture morphs between different textures.We further show an interactive texture tool that allows a user to adjust local characteristics of the synthesized texture by directly using the co-occurrence values.展开更多
基金supported by the Mid-Career Researcher program through the National Research Foundation of Korea(NRF)funded by the MSIT(Ministry of Science and ICT)under Grant 2020R1A2C2014336.
文摘In the proposed paper,a parallel structure type Generative Adversarial Network(GAN)using edge and texture information is proposed.In the existing GAN-based model,many learning iterations had to be given to obtaining an output that was somewhat close to the original data,and noise and distortion occurred in the output image even when learning was performed.To solve this problem,the proposed model consists of two generators and three discriminators to propose a network in the form of a parallel structure.In the network,each edge information and texture information were received as inputs,learning was performed,and each character was combined and outputted through the Combine Discriminator.Through this,edge information and distortion of the output image were improved even with fewer iterations than DCGAN,which is the existing GAN-based model.As a result of learning on the network of the proposed model,a clear image with improved contour and distortion of objects in the image was output from about 50,000 iterations.
基金This work was supported in part by the Basic Scientific Research Project of Liaoning Provincial Department of Education under Grant Nos.LJKQZ2021152 and LJ2020JCL007in part by the National Science Foundation of China(NSFC)under Grant No.61602226in part by the PhD Startup Foundation of Liaoning Technical University of China under Grant Nos.18-1021.
文摘To address the problems of lack of high-frequency information and texture details and unstable training in superresolution generative adversarial net-works,this paper optimizes the generator and discriminator based on the SRGAN model.First,the residual dense block is used as the basic structural unit of the gen-erator to improve the network’s feature extraction capability.Second,enhanced lightweight coordinate attention is incorporated to help the network more precisely concentrate on high-frequency location information,thereby allowing the gener-ator to produce more realistic image reconstruction results.Then,we propose a symmetric and efficient pyramidal segmentation attention discriminator network in which the attention mechanism is capable of derivingfiner-grained multiscale spatial information and creating long-term dependencies between multiscale chan-nel attentions,thus enhancing the discriminative ability of the network.Finally,a Charbonnier loss function and a gradient variance loss function with improved robustness are used to better realize the image’s texture structure and enhance the model’s stability.Thefindings from the experiments reveal that the reconstructed image quality enhances the average peak signal-to-noise ratio(PSNR)by 1.59 dB and the structural similarity index(SSIM)by 0.045 when compared to SRGAN on the three test sets.Compared with the state-of-the-art methods,the reconstructed images have a clearer texture structure,richer high-frequency details,and better visual effects.
文摘基于图像的物体三维重建一直是计算机视觉领域的研究热点.与二维人脸图像相比,三维人脸模型能够承载更多的信息从而具有更广泛的应用前景,如更精准的身份信息识别标志、更准确的情感表达媒介等.为了从单幅大视角的二维人脸图像中重建出具有真实感的三维彩色人脸模型,提出一种结构简单但有效的算法.首先设计一个编码-解码网络,从原始RGB图像生成并记录完整的三维人脸信息的二维UV位置图;然后使用一个卷积神经网络从中重塑出三维人脸;最后考虑人脸大视角时的自遮挡情况,进一步通过条件生成对抗网络补全UV纹理图的缺失.使用Stirling/ESRC 3D Face Database与其他三维人脸重建的算法进行对比实验,结果表明,所提算法能够实现更高的重建精度,特别是在大视角人脸图像重建应用中,即使在复杂环境下也可以获得完整和真实的三维人脸模型.
文摘针对3D人脸重建方法在贴图时忽视对纹理处理的设计,仅进行仿射变换和插值,其中仿射变换会导致其生成图像的高频分量遭到损坏,尤其是给出嘴部姿态不同的源、目标人像时,会造成人像的嘴部纹理缺失,而插值方法会造成灰度不连续现象;提出一种增强的3D人脸替换方法,称为基于生成-重建的人脸替换(generative reconstructed face swap,GRFS)。GRFS将对抗生成网络应用于对3D人脸替换结果的纹理修复,包括两个子网络:嘴部修复网络(mouth restoration network,MRN)以及局部修复生成网络(generative local restoration network,GLRN)。MRN用于修复人像的嘴部细节,GLRN用于修复3D人脸重建过程中损坏的高频分量,并使得异常的不连续灰度变得光滑。实验结果表明,GRFS可以在给定单对源、目标人像的情况下生成逼真的人脸替换结果,且在不同实验环境下的表现好于主流人脸替换算法。
文摘As image generation techniques mature,there is a growing interest in explainable representations that are easy to understand and intuitive to manipulate.In this work,we turn to co-occurrence statistics,which have long been used for texture analysis,to learn a controllable texture synthesis model.We propose a fully convolutional generative adversarial network,conditioned locally on co-occurrence statistics,to generate arbitrarily large images while having local,interpretable control over texture appearance.To encourage fidelity to the input condition,we introduce a novel differentiable co-occurrence loss that is integrated seamlessly into our framework in an end-to-end fashion.We demonstrate that our solution offers a stable,intuitive,and interpretable latent representation for texture synthesis,which can be used to generate smooth texture morphs between different textures.We further show an interactive texture tool that allows a user to adjust local characteristics of the synthesized texture by directly using the co-occurrence values.
