Graphical Models最新文献_第3页

Deep-learning-based point cloud completion methods: A review 基于深度学习的点云补全方法：综述

IF 2.5 4区计算机科学

Graphical Models Pub Date : 2024-10-03 DOI: 10.1016/j.gmod.2024.101233

Kun Zhang , Ao Zhang , Xiaohong Wang , Weisong Li

引用次数: 0

Sketch-2-4D: Sketch driven dynamic 3D scene generation Sketch-2-4D：草图驱动动态 3D 场景生成

IF 2.5 4区计算机科学

Graphical Models Pub Date : 2024-09-16 DOI: 10.1016/j.gmod.2024.101231

Guo-Wei Yang, Dong-Yu Chen, Tai-Jiang Mu

{"title":"Sketch-2-4D: Sketch driven dynamic 3D scene generation","authors":"Guo-Wei Yang, Dong-Yu Chen, Tai-Jiang Mu","doi":"10.1016/j.gmod.2024.101231","DOIUrl":"10.1016/j.gmod.2024.101231","url":null,"abstract":"<div><p>Sketch-based content generation offers flexible controllability, making it a promising narrative avenue in film production. Directors often visualize their imagination by crafting storyboards using sketches and textual descriptions for each shot. However, current video generation methods suffer from three-dimensional inconsistencies, with notably artifacts during large motion or camera pans around scenes. A suitable solution is to directly generate 4D scene, enabling consistent dynamic three-dimensional scenes generation. We define the Sketch-2-4D problem, aiming to enhance controllability and consistency in this context. We propose a novel Control Score Distillation Sampling (SDS-C) for sketch-based 4D scene generation, providing precise control over scene dynamics. We further design Spatial Consistency Modules and Temporal Consistency Modules to tackle the temporal and spatial inconsistencies introduced by sketch-based control, respectively. Extensive experiments have demonstrated the effectiveness of our approach.</p></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"136 ","pages":"Article 101231"},"PeriodicalIF":2.5,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1524070324000195/pdfft?md5=12c973a601d5430e660ae4453ec0a4d8&pid=1-s2.0-S1524070324000195-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142244146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FACE: Feature-preserving CAD model surface reconstruction FACE：保留特征的 CAD 模型表面重建

IF 2.5 4区计算机科学

Graphical Models Pub Date : 2024-09-12 DOI: 10.1016/j.gmod.2024.101230

Shuxian Cai , Yuanyan Ye , Juan Cao , Zhonggui Chen

{"title":"FACE: Feature-preserving CAD model surface reconstruction","authors":"Shuxian Cai , Yuanyan Ye , Juan Cao , Zhonggui Chen","doi":"10.1016/j.gmod.2024.101230","DOIUrl":"10.1016/j.gmod.2024.101230","url":null,"abstract":"<div><p>Feature lines play a pivotal role in the reconstruction of CAD models. Currently, there is a lack of a robust explicit reconstruction algorithm capable of achieving sharp feature reconstruction in point clouds with noise and non-uniformity. In this paper, we propose a feature-preserving CAD model surface reconstruction algorithm, named FACE. The algorithm initiates with preprocessing the point cloud through denoising and resampling steps, resulting in a high-quality point cloud that is devoid of noise and uniformly distributed. Then, it employs discrete optimal transport to detect feature regions and subsequently generates dense points along potential feature lines to enhance features. Finally, the advancing-front surface reconstruction method, based on normal vector directions, is applied to reconstruct the enhanced point cloud. Extensive experiments demonstrate that, for contaminated point clouds, this algorithm excels not only in reconstructing straight edges and corner points but also in handling curved edges and surfaces, surpassing existing methods.</p></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"136 ","pages":"Article 101230"},"PeriodicalIF":2.5,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1524070324000183/pdfft?md5=c92c397f0636a8c7097baed24a31ef77&pid=1-s2.0-S1524070324000183-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142171715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Image vectorization using a sparse patch layout 使用稀疏补丁布局进行图像矢量化

IF 2.5 4区计算机科学

Graphical Models Pub Date : 2024-09-05 DOI: 10.1016/j.gmod.2024.101229

K. He, J.B.T.M. Roerdink, J. Kosinka

引用次数: 0

Corrigendum to Image restoration for digital line drawings using line masks [Graphical Models 135 (2024) 101226] 使用线条掩码进行数字线条图的图像修复[图形模型 135 (2024) 101226] 更正

IF 2.5 4区计算机科学

Graphical Models Pub Date : 2024-09-02 DOI: 10.1016/j.gmod.2024.101228

Yan Zhu, Yasushi Yamaguchi

引用次数: 0

Image restoration for digital line drawings using line masks 使用线条遮罩修复数字线条图的图像

IF 2.5 4区计算机科学

Graphical Models Pub Date : 2024-08-20 DOI: 10.1016/j.gmod.2024.101226

Yan Zhu, Yasushi Yamaguchi

{"title":"Image restoration for digital line drawings using line masks","authors":"Yan Zhu, Yasushi Yamaguchi","doi":"10.1016/j.gmod.2024.101226","DOIUrl":"10.1016/j.gmod.2024.101226","url":null,"abstract":"<div><p>The restoration of digital images holds practical significance due to the fact that degradation of digital image data on the internet is common. State-of-the-art image restoration methods usually employ end-to-end trained networks. However, we argue that a network trained with diverse image pairs is not optimal for restoring line drawings which have extensive plain backgrounds. We propose a line-drawing restoration framework which takes a restoration neural network as backbone and processes an input degraded line drawing in two steps. First, a proposed mask-predicting network predicts a line mask which indicates the possible location of foreground and background in the potential original line drawing. Next, we feed the degraded input line drawing together with the predicted line mask into the backbone restoration network. The traditional <span><math><msub><mrow><mi>L</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span> loss for the backbone restoration network is substituted with a masked Mean Square Error (MSE) loss. We test our framework on two classical image restoration tasks: JPEG restoration and super-resolution, and experiments demonstrate that our framework can achieve better quantitative and visual results in most cases.</p></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"135 ","pages":"Article 101226"},"PeriodicalIF":2.5,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1524070324000146/pdfft?md5=58619f9331f768a8dedffc9dc70f4dbb&pid=1-s2.0-S1524070324000146-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142012112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reconstruction of the bending line for free-form bent components extracting the centroids and exploiting NURBS curves 通过提取中心点和利用 NURBS 曲线重构自由形态弯曲部件的弯曲线

IF 2.5 4区计算机科学

Graphical Models Pub Date : 2024-08-19 DOI: 10.1016/j.gmod.2024.101227

Lorenzo Scandola, Maximilian Erber, Philipp Hagenlocher, Florian Steinlehner, Wolfram Volk

{"title":"Reconstruction of the bending line for free-form bent components extracting the centroids and exploiting NURBS curves","authors":"Lorenzo Scandola, Maximilian Erber, Philipp Hagenlocher, Florian Steinlehner, Wolfram Volk","doi":"10.1016/j.gmod.2024.101227","DOIUrl":"10.1016/j.gmod.2024.101227","url":null,"abstract":"<div><p>Free-form bending belongs to the kinematics-based forming processes and allows the manufacturing of arbitrary 3D-bent components. To obtain the desired part, the tool kinematics is adjusted by comparing the target and obtained bending line. While the target geometry consists of parametric CAD data, the obtained geometry is a surface mesh, making the bending line extraction a challenging task. In this paper the reconstruction of the bending line for free-form bent components is presented. The strategy relies on the extraction of the centroids, for which a ray casting algorithm is developed and compared to an existing Voronoi-based method. Subsequently the obtained points are used to fit a NURBS parametric model of the curve. The algorithm parameters are investigated with a sensitivity analysis, and its performance is evaluated with a defined error metric. Finally, the strategy is validated comparing its results with a Voronoi-based algorithm, and investigating different cross-sections and geometries.</p></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"135 ","pages":"Article 101227"},"PeriodicalIF":2.5,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1524070324000158/pdfft?md5=5ae58aca47e71146ef63b6cd34d29835&pid=1-s2.0-S1524070324000158-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142006829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mesh deformation-based single-view 3D reconstruction of thin eyeglasses frames with differentiable rendering 基于网格变形的单视角薄眼镜架三维重建与可变渲染

IF 2.5 4区计算机科学

Graphical Models Pub Date : 2024-08-09 DOI: 10.1016/j.gmod.2024.101225

Fan Zhang , Ziyue Ji , Weiguang Kang , Weiqing Li , Zhiyong Su

{"title":"Mesh deformation-based single-view 3D reconstruction of thin eyeglasses frames with differentiable rendering","authors":"Fan Zhang , Ziyue Ji , Weiguang Kang , Weiqing Li , Zhiyong Su","doi":"10.1016/j.gmod.2024.101225","DOIUrl":"10.1016/j.gmod.2024.101225","url":null,"abstract":"<div><p>With the support of Virtual Reality (VR) and Augmented Reality (AR) technologies, the 3D virtual eyeglasses try-on application is well on its way to becoming a new trending solution that offers a “try on” option to select the perfect pair of eyeglasses at the comfort of your own home. Reconstructing eyeglasses frames from a single image with traditional depth and image-based methods is extremely difficult due to their unique characteristics such as lack of sufficient texture features, thin elements, and severe self-occlusions. In this paper, we propose the first mesh deformation-based reconstruction framework for recovering high-precision 3D full-frame eyeglasses models from a single RGB image, leveraging prior and domain-specific knowledge. Specifically, based on the construction of a synthetic eyeglasses frame dataset, we first define a class-specific eyeglasses frame template with pre-defined keypoints. Then, given an input eyeglasses frame image with thin structure and few texture features, we design a keypoint detector and refiner to detect predefined keypoints in a coarse-to-fine manner to estimate the camera pose accurately. After that, using differentiable rendering, we propose a novel optimization approach for producing correct geometry by progressively performing free-form deformation (FFD) on the template mesh. We define a series of loss functions to enforce consistency between the rendered result and the corresponding RGB input, utilizing constraints from inherent structure, silhouettes, keypoints, per-pixel shading information, and so on. Experimental results on both the synthetic dataset and real images demonstrate the effectiveness of the proposed algorithm.</p></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"135 ","pages":"Article 101225"},"PeriodicalIF":2.5,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1524070324000134/pdfft?md5=429e33b8e8d8f39cf8d47fa19b9c19f2&pid=1-s2.0-S1524070324000134-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High-fidelity instructional fashion image editing 高保真时尚图像编辑教学

IF 2.5 4区计算机科学

Graphical Models Pub Date : 2024-07-30 DOI: 10.1016/j.gmod.2024.101223

Yinglin Zheng , Ting Zhang , Jianmin Bao , Dong Chen , Ming Zeng

{"title":"High-fidelity instructional fashion image editing","authors":"Yinglin Zheng , Ting Zhang , Jianmin Bao , Dong Chen , Ming Zeng","doi":"10.1016/j.gmod.2024.101223","DOIUrl":"10.1016/j.gmod.2024.101223","url":null,"abstract":"<div><p>Instructional image editing has received a significant surge of attention recently. In this work, we are interested in the challenging problem of instructional image editing within the particular fashion realm, a domain with significant potential demand in both commercial and personal contexts. This specific domain presents heightened challenges owing to the stringent quality requirements. It necessitates not only the creation of vivid details in alignment with instructions, but also the preservation of precise attributes unrelated to the text guidance. Naive extensions of existing image editing methods produce noticeable artifacts. In order to achieve high-fidelity fashion editing, we propose a novel framework, leveraging the generative prior of a pre-trained human generator and performing edit in the latent space. In addition, we introduce a novel CLIP-based loss to better align the generated target with the instruction. Extensive experiments demonstrate that our approach outperforms prior works including GAN-based editing as well as diffusion-based editing by a large margin, showing impressive visual quality.</p></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"135 ","pages":"Article 101223"},"PeriodicalIF":2.5,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1524070324000110/pdfft?md5=480bdc352d9fc3901d6a01e1e2794553&pid=1-s2.0-S1524070324000110-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141886752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Make static person walk again via separating pose action from shape 通过将姿势动作与形状分离，让静止的人重新行走

IF 2.5 4区计算机科学

Graphical Models Pub Date : 2024-07-03 DOI: 10.1016/j.gmod.2024.101222

Yongwei Nie , Meihua Zhao , Qing Zhang , Ping Li , Jian Zhu , Hongmin Cai

引用次数: 0