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Automated Video Generation of Moving Digits from Text Using Deep Deconvolutional Generative Adversarial Network

Computers, Materials & Continua 2023年第11期77卷 2359-2383页

作者： Anwar Ullah Xinguo Yu Muhammad Numan National Engineering Research Center for E-Learning Central China Normal UniversityWuhan430079China Wollongong Joint Institute Central China Normal UniversityWuhan430079China

Generating realistic and synthetic video from text is a highly challenging task due to the multitude of issues involved,including digit deformation,noise interference between frames,blurred output,and the need for temporal coherence across *** this paper,we propose a novel approach for generating coherent videos of moving digits from textual input using a Deep Deconvolutional Generative Adversarial Network(DD-GAN).The DDGAN comprises a Deep Deconvolutional Neural Network(DDNN)as a Generator(G)and a modified Deep Convolutional Neural Network(DCNN)as a Discriminator(D)to ensure temporal coherence between adjacent *** proposed research involves several ***,the input text is fed into a Long Short Term Memory(LSTM)based text encoder and then smoothed using Conditioning Augmentation(CA)techniques to enhance the effectiveness of the Generator(G).Next,using a DDNN to generate video frames by incorporating enhanced text and random noise and modifying a DCNN to act as a Discriminator(D),effectively distinguishing between generated and real *** research evaluates the quality of the generated videos using standard metrics like Inception Score(IS),Fréchet Inception Distance(FID),Fréchet Inception Distance for video(FID2vid),and Generative Adversarial Metric(GAM),along with a human study based on realism,coherence,and *** conducting experiments on Single-Digit Bouncing MNIST GIFs(SBMG),Two-Digit Bouncing MNIST GIFs(TBMG),and a custom dataset of essential mathematics videos with related text,this research demonstrates significant improvements in both metrics and human study results,confirming the effectiveness of *** research also took the exciting challenge of generating preschool math videos from text,handling complex structures,digits,and symbols,and achieving successful *** proposed research demonstrates promising results for generating coherent videos from textual input.

关键词： Generative Adversarial Network(GAN) deconvolutional neural network convolutional neural network Inception Score(IS) temporal coherence Fréchet Inception Distance(FID) Generative Adversarial Metric(GAM)

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Surveillance Video Defogging Algorithm Optimized by Background Extraction

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Journal of Systems Science and Information 2022年第4期10卷 410-424页

作者： Hong GUO Xiaochun WANG Hongjun LI College of Sciences Beijing Forestry UniversityBeijing 100083China

To reduce the flicker artifacts caused by video defogging,a surveillance video defogging algorithm based on the background extraction and consistent constraints is ***,an inter frame consistency constraint is constructed and applied to background ***,the extracted background is defogged with an improved static defogging ***,the foreground is extracted using the extracted background and further defogged using constraints of the consistency between the foreground and *** results show that our algorithm can remove fog effectively and preserve the temporal coherence well.

关键词： video defogging video dehazing background modeling temporal coherence

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Stylized strokes for coherent line drawings

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Computational Visual Media 2015年第1期1卷 79-89页

作者： Liming Lou Lu Wang Xiangxu Meng School of Computer Science and Technology Shandong University

temporal coherence is one of the central challenges for rendering a stylized line. It is especially difficult for stylized contours of coarse meshes or nonuniformly sampled models, because those contours are polygonal feature edges on the models with no continuous correspondences between frames. We describe a novel and simple technique for constructing a 2D brush path along a 3D contour. We also introduce a 3D parameter propagation and re-parameterization procedure to construct stroke paths along the 2D brush path to draw coherently stylized feature lines with a wide range of styles. Our method runs in real-time for coarse or non-uniformly sampled models, making it suitable for interactive applications needing temporal coherence.

关键词： non-photorealistic rendering(NPR) line drawings temporal coherence stylized strokes

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