COMPUTER GRAPHICS FORUM 是 EUROGRAPHICS 的官方期刊，与 WILEY-BLACKWELL 合作出版，是对全球图形开发感兴趣的计算机图形专业人士的独特国际信息来源。它现在是商业和学术环境中计算机图形研究人员、开发人员和用户的主要期刊之一。该杂志报道了世界范围内该领域的最新发展，涵盖了计算机图形学理论、实践和应用的各个方面。

Computer Graphics Forum is the official journal of EUROGRAPHICS, published in collaboration with WILEY-BLACKWELL. It serves as a unique international source of information for computer graphics professionals interested in global graphics development. It is now one of the leading journals for researchers, developers, and users in both commercial and academic settings. The journal reports on the latest developments in the field, covering all aspects of computer graphics theory, practice, and applications.

《Computer Graphics Forum》是EUROGRAPHICS的官方期刊，与WILEY-BLACKWELL合作出版，成为全球计算机图形专业人士获取信息的独特国际来源。它是商业和学术环境中计算机图形研究人员、开发人员和用户的重要期刊之一。该杂志报道了该领域的最新发展，涵盖了计算机图形学的理论、实践和应用的各个方面。

Computer Graphics Forum

Abstract We propose a simple yet effective method to perform surface remeshing with hard constraints, such as bounding approximation errors and ensuring Delaunay conditions. The remeshing is formulated as a constrained optimization problem, where the variables contain the mesh connectivity and the mesh geometry. To solve it effectively, we adopt traditional local operations, including edge split, edge collapse, edge flip, and vertex relocation, to update the variables. Central to our method is an evolutionary vertex optimization algorithm, which is derivative‐free and robust. The feasibility and practicability of our method are demonstrated in two applications, including error‐bounded Delaunay mesh simplification and error‐bounded angle improvement with a given number of vertices, over many models. Compared to state‐of‐the‐art methods, our method achieves higher remeshing quality.

Wen‐Xiang Zhang

Qi Wang

Jia‐Peng Guo

Shuangming Chai

Ligang Liu

Xiao‐Ming Fu

Constrained Remeshing Using Evolutionary Vertex Optimization

Abstract We present an example‐based crowd simulation technique. Most crowd simulation techniques assume that the behavior exhibited by each person in the crowd can be defined by a restricted set of rules. This assumption limits the behavioral complexity of the simulated agents. By learning from real‐world examples, our autonomous agents display complex natural behaviors that are often missing in crowd simulations. Examples are created from tracked video segments of real pedestrian crowds. During a simulation, autonomous agents search for examples that closely match the situation that they are facing. Trajectories taken by real people in similar situations, are copied to the simulated agents, resulting in seemingly natural behaviors.

Alon Lerner

Yiorgos Chrysanthou

Dani Lischinski

Crowds by Example

示例人群

Abstract Environment maps are commonly used to represent and compute far‐field illumination in virtual scenes. However, they are expensive to evaluate and sample from, limiting their applicability to real‐time rendering. Previous methods have focused on compression through spherical‐domain approximations, or on learning priors for natural, day‐light illumination. These hinder both accuracy and generality, and do not provide the probability information required for importance‐sampling Monte Carlo integration. We propose NEnv, a deep‐learning fully‐differentiable method, capable of compressing and learning to sample from a single environment map. NEnv is composed of two different neural networks: A normalizing flow, able to map samples from uniform distributions to the probability density of the illumination, also providing their corresponding probabilities; and an implicit neural representation which compresses the environment map into an efficient differentiable function. The computation time of environment samples with NEnv is two orders of magnitude less than with traditional methods. NEnv makes no assumptions regarding the content (i.e. natural illumination), thus achieving higher generality than previous learning‐based approaches. We share our implementation and a diverse dataset of trained neural environment maps, which can be easily integrated into existing rendering engines.

Carlos Rodriguez‐Pardo

Javier Fabre

Elena Garces

Jorge Lopez‐Moreno

NEnv: Neural Environment Maps for Global Illumination

Abstract Data acquisition in large‐scale scenes regularly involves accumulating information across multiple scans. A common approach is to locally align scan pairs using Iterative Closest Point (ICP) algorithm (or its variants), but requires static scenes and small motion between scan pairs. This prevents accumulating data across multiple scan sessions and/or different acquisition modalities (e.g., stereo, depth scans). Alternatively, one can use a global registration algorithm allowing scans to be in arbitrary initial poses. The state‐of‐the‐art global registration algorithm, 4PCS, however has a quadratic time complexity in the number of data points. This vastly limits its applicability to acquisition of large environments. We present S uper 4PCS for global pointcloud registration that is optimal, i.e., runs in linear time (in the number of data points) and is also output sensitive in the complexity of the alignment problem based on the (unknown) overlap across scan pairs. Technically, we map the algorithm as an ‘instance problem’ and solve it efficiently using a smart indexing data organization. The algorithm is simple, memory‐efficient, and fast. We demonstrate that S uper 4PCS results in significant speedup over alternative approaches and allows unstructured efficient acquisition of scenes at scales previously not possible. Complete source code and datasets are available for research use at http://geometry.cs.ucl.ac.uk/projects/2014/super4PCS/ .

Nicolas Mellado

Dror Aiger

Niloy J. Mitra

Super 4PCS Fast Global Pointcloud Registration via Smart Indexing

基于智能索引的Super 4PCS快速全局点云配准

Computer Graphics ForumVolume 24, Issue 3 p. 635-645 Lightness Perception in Tone Reproduction for High Dynamic Range Images Grzegorz Krawczyk, Grzegorz Krawczyk MPI Informatik, Saarbrücken, Germany † e-mail: [email protected]Search for more papers by this authorKarol Myszkowski, Karol Myszkowski MPI Informatik, Saarbrücken, Germany ‡ e-mail: [email protected]Search for more papers by this authorHans-Peter Seidel, Hans-Peter Seidel MPI Informatik, Saarbrücken, Germany § e-mail: [email protected]Search for more papers by this author Grzegorz Krawczyk, Grzegorz Krawczyk MPI Informatik, Saarbrücken, Germany † e-mail: [email protected]Search for more papers by this authorKarol Myszkowski, Karol Myszkowski MPI Informatik, Saarbrücken, Germany ‡ e-mail: [email protected]Search for more papers by this authorHans-Peter Seidel, Hans-Peter Seidel MPI Informatik, Saarbrücken, Germany § e-mail: [email protected]Search for more papers by this author First published: 07 October 2005 https://doi.org/10.1111/j.1467-8659.2005.00888.xCitations: 53 Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Citing Literature Volume24, Issue3September 2005Pages 635-645 RelatedInformation

Grzegorz Krawczyk

Karol Myszkowski

Hans‐Peter Seidel

Lightness Perception in Tone Reproduction for High Dynamic Range Images

高动态范围图像的色调再现中的明度感知

Abstract Novel view synthesis for talking heads presents significant challenges due to the complex and diverse motion transformations involved. Conventional methods often resort to reliance on structure priors, like facial templates, to warp observed images into a canonical space conducive to rendering. However, the incorporation of such priors introduces a trade‐off‐while aiding in synthesis, they concurrently amplify model complexity, limiting generalizability to other deformable scenes. Departing from this paradigm, we introduce a pioneering solution: the motion‐conditioned neural radiance field, MoNeRF, designed to model talking heads through latent motion navigation. At the core of MoNeRF lies a novel approach utilizing a compact set of latent codes to represent orthogonal motion directions. This innovative strategy empowers MoNeRF to efficiently capture and depict intricate scene motion by linearly combining these latent codes. In an extended capability, MoNeRF facilitates motion control through latent code adjustments, supports view transfer based on reference videos, and seamlessly extends its applicability to model human bodies without necessitating structural modifications. Rigorous quantitative and qualitative experiments unequivocally demonstrate MoNeRF's superior performance compared to state‐of‐the‐art methods in talking head synthesis. We will release the source code upon publication.

X. Li

Y. Ding

R. Li

Z. Tang

K. Li

MoNeRF: Deformable Neural Rendering for Talking Heads via Latent Motion Navigation

MoNeRF: 通过潜在运动导航实现可变形神经渲染的头部对话

Abstract Research shows that user traits can modulate the use of visualization systems and have a measurable influence on users' accuracy, speed, and attention when performing visual analysis. This highlights the importance of user‐adaptive visualization that can modify themselves to the characteristics and preferences of the user. However, there are very few such visualization systems, as creating them requires broad knowledge from various sub‐domains of the visualization community. A user‐adaptive system must consider which user traits they adapt to, their adaptation logic and the types of interventions they support. In this STAR, we survey a broad space of existing literature and consolidate them to structure the process of creating user‐adaptive visualizations into five components: Capture Ⓐ Input from the user and any relevant peripheral information. Perform computational Ⓑ User Modelling with this input to construct a Ⓒ User Representation . Employ Ⓓ Adaptation Assignment logic to identify when and how to introduce Ⓔ Interventions . Our novel taxonomy provides a road map for work in this area, describing the rich space of current approaches and highlighting open areas for future work.

Fernando Yanez

Cristina Conati

Alvitta Ottley

Carolina Nobre

The State of the Art in User‐Adaptive Visualizations

用户自适应可视化技术的最新进展

Abstract User confidence plays an important role in guided visual data analysis scenarios, especially when uncertainty is involved in the analytical process. However, measuring confidence in practical scenarios remains an open challenge, as previous work relies primarily on self‐reporting methods. In this work, we propose a quantitative approach to measure user confidence—as opposed to trust—in an analytical scenario. We do so by exploiting the respective user interaction provenance graph and examining the impact of guidance using a set of network metrics. We assess the usefulness of our proposed metrics through a user study that correlates results obtained from self‐reported confidence assessments and our metrics—both with and without guidance. The results suggest that our metrics improve the evaluation of user confidence compared to available approaches. In particular, we found a correlation between self‐reported confidence and some of the proposed provenance network metrics. The quantitative results, though, do not show a statistically significant impact of the guidance on user confidence. An additional descriptive analysis suggests that guidance could impact users' confidence and that the qualitative analysis of the provenance network topology can provide a comprehensive view of changes in user confidence. Our results indicate that our proposed metrics and the provenance network graph representation support the evaluation of user confidence and, subsequently, the effective development of guidance in VA.

M. Musleh

D. Ceneda

H. Ehlers

R. G. Raidou

ConAn: Measuring and Evaluating User Confidence in Visual Data Analysis Under Uncertainty

ConAn：在不确定性下测量和评估用户对可视化数据分析的信心

Abstract We present a computational approach for unfolding 3D shapes isometrically into the plane as a single patch without overlapping triangles. This is a hard, sometimes impossible, problem, which existing methods are forced to soften by allowing for map distortions or multiple patches. Instead, we propose a geometric relaxation of the problem: We modify the input shape until it admits an overlap‐free unfolding. We achieve this by locally displacing vertices and collapsing edges, guided by the unfolding process. We validate our algorithm quantitatively and qualitatively on a large dataset of complex shapes and show its proficiency by fabricating real shapes from paper.

M. Bhargava

C. Schreck

M. Freire

P. A. Hugron

S. Lefebvre

S. Sellán

B. Bickel

Mesh Simplification for Unfolding<sup>*</sup>

网格简化用于展开<sup>*</sup>

Abstract Automatic font generation aims to streamline the design process by creating new fonts with minimal style references. This technology significantly reduces the manual labour and costs associated with traditional font design. Image‐to‐image translation has been the dominant approach, transforming font images from a source style to a target style using a few reference images. However, this framework struggles to fully decouple content from style, particularly when dealing with significant style shifts. Despite these limitations, image‐to‐image translation remains prevalent due to two main challenges faced by conditional generative models: (1) inability to handle unseen characters and (2) difficulty in providing precise content representations equivalent to the source font. Our approach tackles these issues by leveraging recent advancements in Chinese character representation research to pre‐train a robust content representation model. This model not only handles unseen characters but also generalizes to non‐existent ones, a capability absent in traditional image‐to‐image translation. We further propose a Transformer‐based Style Filter that not only accurately captures stylistic features from reference images but also handles any combination of them, fostering greater convenience for practical automated font generation applications. Additionally, we incorporate content loss with commonly used pixel‐ and perceptual‐level losses to refine the generated results from a comprehensive perspective. Extensive experiments validate the effectiveness of our method, particularly its ability to handle unseen characters, demonstrating significant performance gains over existing state‐of‐the‐art methods.

Abbreviation	Comput. Graph. Forum.
Journal Impact	2.58
Quartiles(Global)	COMPUTER SCIENCE, SOFTWARE ENGINEERING(Q2)

ISSN	0167-7055, 1467-8659
h-index	139

Publication Information	Publisher: Wiley-Blackwell Publishing Ltd，Publishing cycle: Bimonthly，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 1982，Proportion of original research papers: 100.00%，Self Citation Rate:11.10%， Gold OA Rate: 28.26%
Average review cycle	网友分享经验：一般,3-6周
Average recruitment ratio	网友分享经验：容易

Computer Graphics Forum

Journal Citation Format

A journal article with 1 author

A journal article with 2 authors

A journal article with 3 authors

A journal article with 5 or more authors

Books Citation Format

Thesis Citation Format

Web sites Citation Format

Patent Citation Format

Staying up late manually editing references? ivySCI automatically matches journals and helps you generate references with a single click.

Share Submission Experience

Constrained Remeshing Using Evolutionary Vertex Optimization

Crowds by Example

NEnv: Neural Environment Maps for Global Illumination

Super 4PCS Fast Global Pointcloud Registration via Smart Indexing

Lightness Perception in Tone Reproduction for High Dynamic Range Images

MoNeRF: Deformable Neural Rendering for Talking Heads via Latent Motion Navigation

The State of the Art in User‐Adaptive Visualizations

ConAn: Measuring and Evaluating User Confidence in Visual Data Analysis Under Uncertainty

Mesh Simplification for Unfolding^*

Conditional Font Generation With Content Pre‐Train and Style Filter

Computer Graphics Forum

Journal Citation Format

A journal article with 1 author

A journal article with 2 authors

A journal article with 3 authors

A journal article with 5 or more authors

Books Citation Format

Thesis Citation Format

Web sites Citation Format

Patent Citation Format

Staying up late manually editing references? ivySCI automatically matches journals and helps you generate references with a single click.

Share Submission Experience

Constrained Remeshing Using Evolutionary Vertex Optimization

Crowds by Example

NEnv: Neural Environment Maps for Global Illumination

Super 4PCS Fast Global Pointcloud Registration via Smart Indexing

Lightness Perception in Tone Reproduction for High Dynamic Range Images

MoNeRF: Deformable Neural Rendering for Talking Heads via Latent Motion Navigation

The State of the Art in User‐Adaptive Visualizations

ConAn: Measuring and Evaluating User Confidence in Visual Data Analysis Under Uncertainty

Mesh Simplification for Unfolding*

Conditional Font Generation With Content Pre‐Train and Style Filter

Mesh Simplification for Unfolding^*