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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 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2024-07-30 DOI: 10.1016/j.gmod.2024.101223
Yinglin Zheng , Ting Zhang , Jianmin Bao , Dong Chen , Ming Zeng

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.

最近,教学图像编辑受到了广泛关注。在这项工作中,我们关注的是在特定时尚领域中进行教学图像编辑这一具有挑战性的问题,该领域在商业和个人方面都有巨大的潜在需求。由于对质量的严格要求,这一特定领域面临着更大的挑战。它不仅需要根据说明创建生动的细节,还需要保留与文本指导无关的精确属性。现有图像编辑方法的简单扩展会产生明显的人工痕迹。为了实现高保真时装编辑,我们提出了一个新颖的框架,利用预先训练好的人类生成器的生成先验,在潜空间中进行编辑。此外,我们还引入了一种新颖的基于 CLIP 的损失,使生成的目标与指令更好地保持一致。广泛的实验证明,我们的方法远远优于之前的工作,包括基于 GAN 的编辑和基于扩散的编辑,显示出令人印象深刻的视觉质量。
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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 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2024-07-03 DOI: 10.1016/j.gmod.2024.101222
Yongwei Nie , Meihua Zhao , Qing Zhang , Ping Li , Jian Zhu , Hongmin Cai

This paper addresses the problem of animating a person in static images, the core task of which is to infer future poses for the person. Existing approaches predict future poses in the 2D space, suffering from entanglement of pose action and shape. We propose a method that generates actions in the 3D space and then transfers them to the 2D person. We first lift the 2D pose of the person to a 3D skeleton, then propose a 3D action synthesis network predicting future skeletons, and finally devise a self-supervised action transfer network that transfers the actions of 3D skeletons to the 2D person. Actions generated in the 3D space look plausible and vivid. More importantly, self-supervised action transfer allows our method to be trained only on a 3D MoCap dataset while being able to process images in different domains. Experiments on three image datasets validate the effectiveness of our method.

本文探讨了在静态图像中制作人物动画的问题,其核心任务是推断人物的未来姿势。现有的方法是在二维空间中预测未来的姿势,存在姿势动作和形状的纠缠问题。我们提出了一种在三维空间生成动作,然后将其转移到二维人物的方法。我们首先将人的二维姿势提升为三维骨架,然后提出一个预测未来骨架的三维动作合成网络,最后设计一个自我监督的动作转移网络,将三维骨架的动作转移到二维人身上。在三维空间中生成的动作看起来合理而生动。更重要的是,自监督动作转移使我们的方法只需在三维 MoCap 数据集上进行训练,就能处理不同领域的图像。在三个图像数据集上的实验验证了我们方法的有效性。
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引用次数: 0
Bilateral transformer 3D planar recovery 双边变压器三维平面恢复
IF 2.5 4区 计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2024-06-21 DOI: 10.1016/j.gmod.2024.101221
Fei Ren , Chunhua Liao , Zhina Xie

In recent years, deep learning based methods for single image 3D planar recovery have made significant progress, but most of the research has focused on overall plane segmentation performance rather than the accuracy of small scale plane segmentation. In order to solve the problem of feature loss in the feature extraction process of small target object features, a three dimensional planar recovery method based on bilateral transformer was proposed. The two sided network branches capture rich small object target features through different scale sampling, and are used for detecting planar and non-planar regions respectively. In addition, the loss of variational information is used to share the parameters of the bilateral network, which achieves the output consistency of the bilateral network and alleviates the problem of feature loss of small target objects. The method is verified on Scannet and Nyu V2 datasets, and a variety of evaluation indexes are superior to the current popular algorithms, proving the effectiveness of the method in three dimensional planar recovery.

近年来,基于深度学习的单幅图像三维平面恢复方法取得了显著进展,但大部分研究都集中在整体平面分割性能上,而不是小尺度平面分割的精度上。为了解决小目标物体特征提取过程中的特征丢失问题,提出了一种基于双边变换器的三维平面恢复方法。双侧网络分支通过不同尺度采样捕捉丰富的小目标物目标特征,分别用于检测平面区域和非平面区域。此外,利用变异信息的丢失来共享双边网络的参数,实现了双边网络输出的一致性,缓解了小目标物体特征丢失的问题。该方法在 Scannet 和 Nyu V2 数据集上进行了验证,各种评价指标均优于目前流行的算法,证明了该方法在三维平面恢复方面的有效性。
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引用次数: 0
Persistent geometry-topology descriptor for porous structure retrieval based on Heat Kernel Signature 基于热核特征的多孔结构检索持久几何拓扑描述符
IF 1.7 4区 计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2024-06-01 DOI: 10.1016/j.gmod.2024.101219
Peisheng Zhuo , Zitong He , Hongwei Lin
<div><p>Porous structures are essential in a variety of fields such as materials science and chemistry. To retrieve porous materials efficiently, novel descriptors are required to quantify the geometric and topological features. In this paper, we present a novel framework to create a descriptor that incorporates both topological and geometric information of a porous structure. To capture geometric information, we keep track of the <span><math><mrow><mi>b</mi><mi>i</mi><mi>r</mi><mi>t</mi><mi>h</mi><mspace></mspace><mspace></mspace><mi>t</mi><mi>i</mi><mi>m</mi><mi>e</mi></mrow></math></span> and <span><math><mrow><mi>d</mi><mi>e</mi><mi>a</mi><mi>t</mi><mi>h</mi><mspace></mspace><mspace></mspace><mi>t</mi><mi>i</mi><mi>m</mi><mi>e</mi></mrow></math></span> of the <span><math><mrow><mi>p</mi><mi>e</mi><mi>r</mi><mi>s</mi><mi>i</mi><mi>s</mi><mi>t</mi><mi>e</mi><mi>n</mi><mi>t</mi><mspace></mspace><mi>f</mi><mi>e</mi><mi>a</mi><mi>t</mi><mi>u</mi><mi>r</mi><mi>e</mi></mrow></math></span>s of a real-valued function on the surface that evolves with a parameter. Then, we generate the corresponding <span><math><mrow><mi>p</mi><mi>e</mi><mi>r</mi><mi>s</mi><mi>i</mi><mi>s</mi><mi>t</mi><mi>e</mi><mi>n</mi><mi>t</mi><mspace></mspace><mspace></mspace><mi>f</mi><mi>e</mi><mi>a</mi><mi>t</mi><mi>u</mi><mi>r</mi><mi>e</mi><mspace></mspace><mspace></mspace><mi>d</mi><mi>i</mi><mi>a</mi><mi>g</mi><mi>r</mi><mi>a</mi><mi>m</mi></mrow></math></span> (<span><math><mrow><mi>D</mi><mi>g</mi><msub><mrow><mi>m</mi></mrow><mrow><mi>P</mi><mi>F</mi></mrow></msub></mrow></math></span>) and convert it into a vector called <span><math><mrow><mi>p</mi><mi>e</mi><mi>r</mi><mi>s</mi><mi>i</mi><mi>s</mi><mi>t</mi><mi>e</mi><mi>n</mi><mi>c</mi><mi>e</mi><mspace></mspace><mspace></mspace><mi>f</mi><mi>e</mi><mi>a</mi><mi>t</mi><mi>u</mi><mi>r</mi><mi>e</mi><mspace></mspace><mspace></mspace><mi>d</mi><mi>e</mi><mi>s</mi><mi>c</mi><mi>r</mi><mi>i</mi><mi>p</mi><mi>t</mi><mi>o</mi><mi>r</mi></mrow></math></span> (PFD). To extract topological information, we sample points from the pore surface and compute the corresponding persistence diagram, which is then transformed into the Persistence B-Spline Grids (PBSG). Our proposed descriptor, namely <span><math><mrow><mi>p</mi><mi>e</mi><mi>r</mi><mi>s</mi><mi>i</mi><mi>s</mi><mi>t</mi><mi>e</mi><mi>n</mi><mi>t</mi><mspace></mspace><mspace></mspace><mi>g</mi><mi>e</mi><mi>o</mi><mi>m</mi><mi>e</mi><mi>t</mi><mi>r</mi><mi>y</mi><mo>−</mo><mi>t</mi><mi>o</mi><mi>p</mi><mi>o</mi><mi>l</mi><mi>o</mi><mi>g</mi><mi>y</mi><mspace></mspace><mspace></mspace><mi>d</mi><mi>e</mi><mi>s</mi><mi>c</mi><mi>r</mi><mi>i</mi><mi>p</mi><mi>t</mi><mi>o</mi><mi>r</mi></mrow></math></span> (PGTD), is obtained by concatenating PFD with PBSG. In our experiments, we use the heat kernel signature (HKS) as the real-valued function to compute the descriptor. We test the method on a synthetic porous dataset and a zeolite dataset and find that it is competitive compa
多孔结构在材料科学和化学等多个领域都至关重要。为了有效检索多孔材料,需要新颖的描述符来量化几何和拓扑特征。在本文中,我们提出了一个新颖的框架,用于创建一个同时包含多孔结构拓扑和几何信息的描述符。为了捕捉几何信息,我们跟踪表面上随参数变化的实值函数的持久特征的诞生时间和消亡时间。然后,我们生成相应的持久特征图(DgmPF),并将其转换为称为持久特征描述器(PFD)的向量。为了提取拓扑信息,我们从孔隙表面采样点并计算相应的持久图,然后将其转换为持久 B 样条网格(PBSG)。我们提出的描述符,即持久几何拓扑描述符(PGTD),是通过将 PFD 与 PBSG 连接得到的。在实验中,我们使用热核特征(HKS)作为实值函数来计算描述符。我们在一个合成多孔数据集和一个沸石数据集上测试了该方法,发现与其他基于 HKS 的描述符和高级拓扑描述符相比,该方法具有很强的竞争力。
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To capture geometric information, we keep track of the &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;b&lt;/mi&gt;&lt;mi&gt;i&lt;/mi&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mi&gt;h&lt;/mi&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mi&gt;i&lt;/mi&gt;&lt;mi&gt;m&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; and &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;d&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;mi&gt;a&lt;/mi&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mi&gt;h&lt;/mi&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mi&gt;i&lt;/mi&gt;&lt;mi&gt;m&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; of the &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;mi&gt;s&lt;/mi&gt;&lt;mi&gt;i&lt;/mi&gt;&lt;mi&gt;s&lt;/mi&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;mi&gt;n&lt;/mi&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mi&gt;f&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;mi&gt;a&lt;/mi&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mi&gt;u&lt;/mi&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;s of a real-valued function on the surface that evolves with a parameter. Then, we generate the corresponding &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;mi&gt;s&lt;/mi&gt;&lt;mi&gt;i&lt;/mi&gt;&lt;mi&gt;s&lt;/mi&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;mi&gt;n&lt;/mi&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mi&gt;f&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;mi&gt;a&lt;/mi&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mi&gt;u&lt;/mi&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mi&gt;d&lt;/mi&gt;&lt;mi&gt;i&lt;/mi&gt;&lt;mi&gt;a&lt;/mi&gt;&lt;mi&gt;g&lt;/mi&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;mi&gt;a&lt;/mi&gt;&lt;mi&gt;m&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;D&lt;/mi&gt;&lt;mi&gt;g&lt;/mi&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;m&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;P&lt;/mi&gt;&lt;mi&gt;F&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;) and convert it into a vector called &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;mi&gt;s&lt;/mi&gt;&lt;mi&gt;i&lt;/mi&gt;&lt;mi&gt;s&lt;/mi&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;mi&gt;n&lt;/mi&gt;&lt;mi&gt;c&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mi&gt;f&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;mi&gt;a&lt;/mi&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mi&gt;u&lt;/mi&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mi&gt;d&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;mi&gt;s&lt;/mi&gt;&lt;mi&gt;c&lt;/mi&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;mi&gt;i&lt;/mi&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mi&gt;o&lt;/mi&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; (PFD). To extract topological information, we sample points from the pore surface and compute the corresponding persistence diagram, which is then transformed into the Persistence B-Spline Grids (PBSG). Our proposed descriptor, namely &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;mi&gt;s&lt;/mi&gt;&lt;mi&gt;i&lt;/mi&gt;&lt;mi&gt;s&lt;/mi&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;mi&gt;n&lt;/mi&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mi&gt;g&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;mi&gt;o&lt;/mi&gt;&lt;mi&gt;m&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;mi&gt;y&lt;/mi&gt;&lt;mo&gt;−&lt;/mo&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mi&gt;o&lt;/mi&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;mi&gt;o&lt;/mi&gt;&lt;mi&gt;l&lt;/mi&gt;&lt;mi&gt;o&lt;/mi&gt;&lt;mi&gt;g&lt;/mi&gt;&lt;mi&gt;y&lt;/mi&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mi&gt;d&lt;/mi&gt;&lt;mi&gt;e&lt;/mi&gt;&lt;mi&gt;s&lt;/mi&gt;&lt;mi&gt;c&lt;/mi&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;mi&gt;i&lt;/mi&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mi&gt;o&lt;/mi&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; (PGTD), is obtained by concatenating PFD with PBSG. In our experiments, we use the heat kernel signature (HKS) as the real-valued function to compute the descriptor. We test the method on a synthetic porous dataset and a zeolite dataset and find that it is competitive compa","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"133 ","pages":"Article 101219"},"PeriodicalIF":1.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1524070324000079/pdfft?md5=499cdacea6ff6d72e1f6c905040f66c2&pid=1-s2.0-S1524070324000079-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141232284","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
An exact algorithm for two-dimensional cutting problems based on multi-level pattern 基于多级模式的二维切割问题精确算法
IF 1.7 4区 计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2024-05-25 DOI: 10.1016/j.gmod.2024.101220
Weiping Pan

A multi-level pattern is proposed for the unconstrained two-dimensional cutting problems of rectangular items, and an exact generation algorithm is constructed. The arrangement of rectangular items with the same type in multiple rows and columns is referred to as a 0-level pattern. An n-level pattern is the horizontal or vertical combination of an n-1 level pattern with a pattern whose level will not exceed n-1. The generation algorithm of multi-level pattern is constructed on the base of dynamic programming, and the multi-level patterns with various sizes are generated with increased level order. The normal size is chosen to reduce unnecessary computation in the algorithm. Three sets of benchmark instances and one set of random production instance from the literatures are used for comparison. Comparing to the exact algorithm in the literature, the results in this paper are equivalent, but the computation time is shorter. Comparing to heuristic algorithms in literatures, the results in this paper are better and the computation time is still good enough for practical applications.

针对矩形物品的无约束二维切割问题,提出了一种多层次模式,并构建了精确的生成算法。同一类型的矩形物品在多行和多列中的排列称为 0 级模式。n 级模式是 n-1 级模式与级别不超过 n-1 的模式的水平或垂直组合。多级图案的生成算法是在动态编程的基础上构建的,不同大小的多级图案随着级序的增加而生成。选择正常大小是为了减少算法中不必要的计算。比较使用了三组基准实例和一组来自文献的随机生产实例。与文献中的精确算法相比,本文的结果相当,但计算时间更短。与文献中的启发式算法相比,本文的结果更好,计算时间也足够实际应用。
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引用次数: 0
Rod-Bonded Discrete Element Method 杆结合离散元素法
IF 1.7 4区 计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2024-04-11 DOI: 10.1016/j.gmod.2024.101218
Kangrui Zhang , Han Yan , Jia-Ming Lu , Bo Ren

The Bonded Discrete Element Method (BDEM) has raised interests in the graphics community in recent years because of its good performance in fracture simulations. However, current explicit BDEM usually needs to work under very small time steps to avoid numerical instability. We propose a new BDEM, namely Rod-BDEM (RBDEM), which uses Cosserat energy and yields integrable forces and torques. We further derive a novel Cosserat rod discretization method to effectively represent the three-dimensional topological connections between discrete elements. Then, a complete implicit BDEM system integrating the appropriate fracture model and contact model is constructed using the implicit Euler integration scheme. Our method allows high Young’s modulus and larger time steps in elastic deformation, breaking, cracking, and impacting, achieving up to 8 times speed up of the total simulation.

近年来,粘结离散元素法(BDEM)因其在断裂模拟中的良好性能而引起了图形学界的兴趣。然而,目前的显式 BDEM 通常需要在非常小的时间步长下工作,以避免数值不稳定性。我们提出了一种新的 BDEM,即 Rod-BDEM (RBDEM),它使用 Cosserat 能量并产生可积分的力和扭矩。我们进一步推导出一种新颖的 Cosserat 杆离散化方法,以有效表示离散元素之间的三维拓扑连接。然后,我们使用隐式欧拉积分方案构建了一个完整的隐式 BDEM 系统,该系统集成了适当的断裂模型和接触模型。我们的方法允许在弹性变形、断裂、开裂和撞击中使用高杨氏模量和更大的时间步长,使总模拟速度提高了 8 倍。
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引用次数: 0
DINA: Deformable INteraction Analogy DINA:可变形交互类比
IF 1.7 4区 计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2024-03-20 DOI: 10.1016/j.gmod.2024.101217
Zeyu Huang , Sisi Dai , Kai Xu , Hao Zhang , Hui Huang , Ruizhen Hu

We introduce deformable interaction analogy (DINA) as a means to generate close interactions between two 3D objects. Given a single demo interaction between an anchor object (e.g. a hand) and a source object (e.g. a mug grasped by the hand), our goal is to generate many analogous 3D interactions between the same anchor object and various new target objects (e.g. a toy airplane), where the anchor object is allowed to be rigid or deformable. To this end, we optimize the pose or shape of the anchor object to adapt it to a new target object to mimic the demo. To facilitate the optimization, we advocate using interaction interface (ITF), defined by a set of points sampled on the anchor object, as a descriptive and robust interaction representation that is amenable to non-rigid deformation. We model similarity between interactions using ITF, while for interaction analogy, we transform the ITF, either rigidly or non-rigidly, to guide the feature matching to the reposing and deformation of the anchor object. Qualitative and quantitative experiments show that our ITF-guided deformable interaction analogy works surprisingly well even with simple distance features compared to variants of state-of-the-art methods that utilize more sophisticated interaction representations and feature learning from large datasets.

我们引入了可变形交互类比(DINA)作为生成两个三维物体之间密切交互的一种手段。给定锚对象(如手)和源对象(如手抓住的杯子)之间的单次演示交互,我们的目标是在同一锚对象和各种新目标对象(如玩具飞机)之间生成许多类似的三维交互,其中锚对象可以是刚性的,也可以是可变形的。为此,我们将优化锚定对象的姿势或形状,使其适应新的目标对象,以模仿演示。为了便于优化,我们主张使用交互界面(ITF),它由锚定对象上的一组点采样定义,是一种可用于非刚性变形的描述性和稳健的交互表示。我们使用 ITF 对交互之间的相似性进行建模,而对于交互类比,我们则对 ITF 进行刚性或非刚性转换,以引导特征匹配与锚定对象的重新摆放和变形相匹配。定性和定量实验表明,与利用更复杂的交互表征和大型数据集特征学习的最先进方法的变体相比,即使是简单的距离特征,我们的 ITF 引导的可变形交互类比也能达到令人惊讶的效果。
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引用次数: 0
Point cloud denoising using a generalized error metric 使用广义误差指标进行点云去噪
IF 1.7 4区 计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2024-03-18 DOI: 10.1016/j.gmod.2024.101216
Qun-Ce Xu , Yong-Liang Yang , Bailin Deng

Effective removal of noises from raw point clouds while preserving geometric features is the key challenge for point cloud denoising. To address this problem, we propose a novel method that jointly optimizes the point positions and normals. To preserve geometric features, our formulation uses a generalized robust error metric to enforce piecewise smoothness of the normal vector field as well as consistency between point positions and normals. By varying the parameter of the error metric, we gradually increase its non-convexity to guide the optimization towards a desirable solution. By combining alternating minimization with a majorization-minimization strategy, we develop a numerical solver for the optimization which guarantees convergence. The effectiveness of our method is demonstrated by extensive comparisons with previous works.

在保留几何特征的同时有效去除原始点云中的噪声是点云去噪的关键挑战。为了解决这个问题,我们提出了一种联合优化点位置和法线的新方法。为了保留几何特征,我们的方法使用了广义的鲁棒误差度量来强制法向量场的片状平滑性以及点位置和法线之间的一致性。通过改变误差度量的参数,我们逐渐增加其非凸性,从而引导优化向理想的解决方案迈进。通过将交替最小化与大化-最小化策略相结合,我们开发出了一种可确保收敛性的优化数值求解器。通过与以往研究成果的广泛比较,我们证明了这一方法的有效性。
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引用次数: 0
Auxetic dihedral Escher tessellations 辅助二面埃舍尔方格网
IF 1.7 4区 计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2024-03-13 DOI: 10.1016/j.gmod.2024.101215
Xiaokang Liu , Lin Lu , Lingxin Cao , Oliver Deussen , Changhe Tu

The auxetic structure demonstrates an unconventional deployable mechanism, expanding in transverse directions while being stretched longitudinally (exhibiting a negative Poisson’s ratio). This characteristic offers advantages in diverse fields such as structural engineering, flexible electronics, and medicine. The rotating (semi-)rigid structure, as a typical auxetic structure, has been introduced into the field of computer-aided design because of its well-defined motion patterns. These structures find application as deployable structures in various endeavors aiming to approximate and rapidly fabricate doubly-curved surfaces, thereby mitigating the challenges associated with their production and transportation. Nevertheless, prior designs relying on basic geometric elements primarily concentrate on exploring the inherent nature of the structure and often lack aesthetic appeal. To address this limitation, we propose a novel design and generation method inspired by dihedral Escher tessellations. By introducing a new metric function, we achieve efficient evaluation of shape deployability as well as filtering of tessellations, followed by a two-step deformation and edge-deployability optimization process to ensure compliance with deployability constraints while preserving semantic meanings. Furthermore, we optimize the shape through physical simulation to guarantee deployability in actual manufacturing and control Poisson’s ratio to a certain extent. Our method yields structures that are both semantically meaningful and aesthetically pleasing, showcasing promising potential for auxetic applications.

这种辅助结构展示了一种非常规的可展开机制,在纵向拉伸的同时横向膨胀(呈现负泊松比)。这一特性在结构工程、柔性电子和医学等多个领域都具有优势。旋转(半)刚性结构是一种典型的辅助结构,由于其运动模式明确,已被引入计算机辅助设计领域。这些结构作为可部署结构应用于各种工作中,旨在近似并快速制造双曲面,从而减轻与生产和运输相关的挑战。尽管如此,以往依靠基本几何元素的设计主要集中在探索结构的固有特性上,往往缺乏美感。针对这一局限性,我们提出了一种新颖的设计和生成方法,其灵感来源于二面埃舍尔方格网。通过引入一个新的度量函数,我们实现了对形状可部署性的高效评估以及对网格的过滤,然后通过两步变形和边缘可部署性优化过程,确保在保留语义的同时符合可部署性约束。此外,我们还通过物理模拟来优化形状,以保证在实际制造中的可部署性,并在一定程度上控制泊松比。我们的方法能产生既有语义意义又美观的结构,为辅助设计应用展示了巨大的潜力。
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引用次数: 0
IGF-Fit: Implicit gradient field fitting for point cloud normal estimation IGF-Fit:用于点云法线估算的隐式梯度场拟合
IF 1.7 4区 计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2024-02-28 DOI: 10.1016/j.gmod.2024.101214
Bowen Lyu , Li-Yong Shen , Chun-Ming Yuan

We introduce IGF-Fit, a novel method for estimating surface normals from point clouds with varying noise and density. Unlike previous approaches that rely on point-wise weights and explicit representations, IGF-Fit employs a network that learns an implicit representation and uses derivatives to predict normals. The input patch serves as both a shape latent vector and query points for fitting the implicit representation. To handle noisy input, we introduce a novel noise transformation module with a training strategy for noise classification and latent vector bias prediction. Our experiments on synthetic and real-world scan datasets demonstrate the effectiveness of IGF-Fit, achieving state-of-the-art performance on both noise-free and density-varying data.

我们介绍了 IGF-Fit,这是一种从具有不同噪声和密度的点云中估计表面法线的新方法。与以往依赖于点权重和显式表示的方法不同,IGF-Fit 采用的是一种学习隐式表示并使用导数预测法线的网络。输入斑块既是形状潜在向量,也是拟合隐式表示的查询点。为了处理噪声输入,我们引入了一个新颖的噪声转换模块,该模块具有噪声分类和潜向量偏差预测的训练策略。我们在合成和实际扫描数据集上进行的实验证明了 IGF-Fit 的有效性,它在无噪声和密度变化数据上都取得了一流的性能。
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Graphical Models
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