In graphic design,layout is a result of the interaction between the design elements in the foreground and background images.However,prevalent research focuses on enhancing the quality of layout generation algorithms,o...In graphic design,layout is a result of the interaction between the design elements in the foreground and background images.However,prevalent research focuses on enhancing the quality of layout generation algorithms,overlooking the interaction and controllability that are essential for designers when applying these methods in realworld situations.This paper proposes a user-centered layout design system,Iris,which provides designers with an interactive environment to expedite the workflow,and this environment encompasses the features of user-constraint specification,layout generation,custom editing,and final rendering.To satisfy the multiple constraints specified by designers,we introduce a novel generation model,multi-constraint LayoutVQ-VAE,for advancing layout generation under intra-and inter-domain constraints.Qualitative and quantitative experiments on our proposed model indicate that it outperforms or is comparable to prevalent state-of-the-art models in multiple aspects.User studies on Iris further demonstrate that the system significantly enhances design efficiency while achieving human-like layout designs.展开更多
Currently,some photorealistic computer graphics are very similar to photographic images.Photorealistic computer generated graphics can be forged as photographic images,causing serious security problems.The aim of this...Currently,some photorealistic computer graphics are very similar to photographic images.Photorealistic computer generated graphics can be forged as photographic images,causing serious security problems.The aim of this work is to use a deep neural network to detect photographic images(PI)versus computer generated graphics(CG).In existing approaches,image feature classification is computationally intensive and fails to achieve realtime analysis.This paper presents an effective approach to automatically identify PI and CG based on deep convolutional neural networks(DCNNs).Compared with some existing methods,the proposed method achieves real-time forensic tasks by deepening the network structure.Experimental results show that this approach can effectively identify PI and CG with average detection accuracy of 98%.展开更多
基金the Alibaba–Zhejiang University Joint Research Institute of Frontier Technologies,China and the Zhejiang–Singapore Innovation and AI Joint Research Lab,China。
文摘In graphic design,layout is a result of the interaction between the design elements in the foreground and background images.However,prevalent research focuses on enhancing the quality of layout generation algorithms,overlooking the interaction and controllability that are essential for designers when applying these methods in realworld situations.This paper proposes a user-centered layout design system,Iris,which provides designers with an interactive environment to expedite the workflow,and this environment encompasses the features of user-constraint specification,layout generation,custom editing,and final rendering.To satisfy the multiple constraints specified by designers,we introduce a novel generation model,multi-constraint LayoutVQ-VAE,for advancing layout generation under intra-and inter-domain constraints.Qualitative and quantitative experiments on our proposed model indicate that it outperforms or is comparable to prevalent state-of-the-art models in multiple aspects.User studies on Iris further demonstrate that the system significantly enhances design efficiency while achieving human-like layout designs.
基金This work is supported,in part,by the National Natural Science Foundation of China under grant numbers U1536206,U1405254,61772283,61602253,61672294,61502242In part,by the Jiangsu Basic Research Programs-Natural Science Foundation under grant numbers BK20150925 and BK20151530+1 种基金In part,by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fundIn part,by the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)fund,China.
文摘Currently,some photorealistic computer graphics are very similar to photographic images.Photorealistic computer generated graphics can be forged as photographic images,causing serious security problems.The aim of this work is to use a deep neural network to detect photographic images(PI)versus computer generated graphics(CG).In existing approaches,image feature classification is computationally intensive and fails to achieve realtime analysis.This paper presents an effective approach to automatically identify PI and CG based on deep convolutional neural networks(DCNNs).Compared with some existing methods,the proposed method achieves real-time forensic tasks by deepening the network structure.Experimental results show that this approach can effectively identify PI and CG with average detection accuracy of 98%.
