Computer Graphics Forum最新文献

{"title":"Real-Time and Controllable Reactive Motion Synthesis via Intention Guidance","authors":"Xiaotang Zhang,&nbsp;Ziyi Chang,&nbsp;Qianhui Men,&nbsp;Hubert P. H. Shum","doi":"10.1111/cgf.70222","DOIUrl":"https://doi.org/10.1111/cgf.70222","url":null,"abstract":"<p>We propose a real-time method for reactive motion synthesis based on the known trajectory of input character, predicting instant reactions using only historical, user-controlled motions. Our method handles the uncertainty of future movements by introducing an intention predictor, which forecasts key joint intentions to make pose prediction more deterministic from the historical interaction. The intention is later encoded into the latent space of its reactive motion, matched with a codebook which represents mappings between input and output. It samples from the categorical distribution for pose generation and strengthens model robustness through adversarial training. Unlike previous offline approaches, the system can recursively generate intentions and reactive motions using feedback from earlier steps, enabling real-time, long-term realistic interactive synthesis. Both quantitative and qualitative experiments show our approach outperforms other matching-based motion synthesis approaches, delivering superior stability and generalisability. In our method, user can also actively influence the outcome by controlling the moving directions, creating a personalised interaction path that deviates from predefined trajectories.</p>","PeriodicalId":10687,"journal":{"name":"Computer Graphics Forum","volume":"44 6","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cgf.70222","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145135431","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}

{"title":"AI-ChartParser: A Method For Extracting Experimental Data From Curve Charts in Academic Papers","authors":"Wenjin Yang,&nbsp;Jie He,&nbsp;Xiaotong Zhang,&nbsp;Haiyan Gong","doi":"10.1111/cgf.70146","DOIUrl":"https://doi.org/10.1111/cgf.70146","url":null,"abstract":"<p>In the fields of engineering and natural sciences, curve charts serve as indispensable visualization tools for scientific research, product development and engineering design, as they encapsulate crucial data necessary for comprehensive analysis. Existing methodologies for data extraction from line charts predominantly depend on single-task models, which frequently exhibit limitations in efficiency and generalization. To overcome these challenges, we propose AI-ChartParser, an end-to-end deep learning model that employs multi-task learning to concurrently execute chart element detection, pivot point detection and curve detection. This approach effectively and efficiently parses diverse chart formats within a cohesive framework. Furthermore, we introduce an Interval-Mean Space-Numerical Mapping algorithm designed to address challenges in data range extraction, thereby significantly minimizing conversion errors. We have incorporated all the methodologies discussed in this paper to develop a comprehensive data extraction tool, facilitating the automatic conversion of line charts into tabular data. Our model exhibits exceptional performance on complex real-world datasets, achieving state-of-the-art accuracy and speed across all three tasks. To facilitate further research, the source codes and pre-trained models are released at https://github.com/ywking/ChartParser.git.</p>","PeriodicalId":10687,"journal":{"name":"Computer Graphics Forum","volume":"44 6","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145135703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vector-Based Terrain Modelling","authors":"Simon Perche,&nbsp;Eric Guérin,&nbsp;Eric Galin,&nbsp;Adrien Peytavie","doi":"10.1111/cgf.70160","DOIUrl":"https://doi.org/10.1111/cgf.70160","url":null,"abstract":"<p>Vector-based graphics offer numerous advantages over grid-based models, including resolution independence and ease of manipulation. Despite these benefits, their use in landscape modelling remains uncommon because of a lack of direct editing and interactive feedback, essential for matching the artist's vision. We introduce a new vector-based model for creating digital terrains based on computationally efficient primitives. We propose a method to convert grid-based digital elevation maps to this representation with a user-defined level of accuracy. Once vectorized, the terrain can be authored using interactive high-level skeleton-based tools adapted to the primitive representation, allowing local deformations that automatically adapt to underlying geomorphological structures and landforms of the terrain.</p>","PeriodicalId":10687,"journal":{"name":"Computer Graphics Forum","volume":"44 6","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145135476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploratory Analysis of Scientific Publications for University Governance","authors":"A. Gràcia,&nbsp;L. Padró,&nbsp;E. Alarcon,&nbsp;P. Vázquez","doi":"10.1111/cgf.70158","DOIUrl":"https://doi.org/10.1111/cgf.70158","url":null,"abstract":"<p>Research-oriented universities often comprise numerous researchers of various types and possess complex research structures that encompass research groups, departments, laboratories, and research institutes. In this situation, understanding the university's strengths and areas of excellence requires careful examination. Additionally, individuals at different levels of governance (e.g., department heads, directors of research institutes, rectors) may seek to establish synergies among researchers to tackle issues such as international project applications or industry technology transfer. University officials and faculty members frequently require the expertise of specific research groups or individuals, but struggle to obtain this information beyond their personal networks. This limits their ability to locate necessary resources effectively. Fortunately, most institutions have databases containing publications that could provide valuable insights into areas of strength within the university. In this article, we present a visual analysis application capable of addressing these questions and assisting management in making informed decisions regarding governance measures such as creating new research institutes. Our system has been evaluated by domain experts, who found it highly beneficial and expressed interest in utilising it regularly.</p>","PeriodicalId":10687,"journal":{"name":"Computer Graphics Forum","volume":"44 6","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cgf.70158","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145135387","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}

{"title":"MPACT: Mesoscopic Profiling and Abstraction of Crowd Trajectories","authors":"Marilena Lemonari,&nbsp;Andreas Panayiotou,&nbsp;Theodoros Kyriakou,&nbsp;Nuria Pelechano,&nbsp;Yiorgos Chrysanthou,&nbsp;Andreas Aristidou,&nbsp;Panayiotis Charalambous","doi":"10.1111/cgf.70156","DOIUrl":"https://doi.org/10.1111/cgf.70156","url":null,"abstract":"<p>Simulating believable crowds for applications like movies or games is challenging due to the many components that comprise a realistic outcome. Users typically need to manually tune a large number of simulation parameters until they reach the desired results. We introduce <i>MPACT</i>, a framework that leverages image-based encoding to convert unlabelled crowd data into meaningful and controllable parameters for crowd generation. In essence, we train a parameter prediction network on a diverse set of synthetic data, which includes pairs of images and corresponding crowd profiles. The learned parameter space enables: (a) implicit crowd authoring and control, allowing users to define desired crowd scenarios using real-world trajectory data, and (b) crowd analysis, facilitating the identification of crowd behaviours in the input and the classification of unseen scenarios through operations within the latent space. We quantitatively and qualitatively evaluate our framework, comparing it against real-world data and selected baselines, while also conducting user studies with expert and novice users. Our experiments show that the generated crowds score high in terms of simulation believability, plausibility and crowd behaviour faithfulness.</p>","PeriodicalId":10687,"journal":{"name":"Computer Graphics Forum","volume":"44 6","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cgf.70156","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145135826","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}

{"title":"Self-Supervised Image Harmonization via Region-Aware Harmony Classification","authors":"Chenyang Tian,&nbsp;Xinbo Wang,&nbsp;Qing Zhang","doi":"10.1111/cgf.70157","DOIUrl":"https://doi.org/10.1111/cgf.70157","url":null,"abstract":"<p>Image harmonization is a widely used technique in image composition, which aims to adjust the appearance of the composited foreground object according to the style of the background image so that the resulting composited image is visually natural and appears to be photographed. Previous methods are mostly trained in a fully supervised manner, while demonstrating promising results, they do not generalize well to complex unseen cases involving significant style and semantic difference between the composited foreground object and the background image. In this paper, we present a self-supervised image harmonization framework that enables superior performance on complex cases. To do so, we first synthesize a large amount of data with wide diversity for training. We then develop an attentive harmonization module to adaptively adjust the foreground appearance by querying relevant background features. To allow more effective image harmonization, we develop a region-aware harmony classifier to explicitly judge whether an image is harmonious or not. Experiments on several datasets show that our method performs favourably against previous methods. Our code will be made publicly available.</p>","PeriodicalId":10687,"journal":{"name":"Computer Graphics Forum","volume":"44 6","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145135808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fluidly Revealing Information: A Survey of Un/foldable Data Visualizations","authors":"M.-J. Bludau,&nbsp;M. Dörk,&nbsp;S. Bruckner,&nbsp;C. Tominski","doi":"10.1111/cgf.70152","DOIUrl":"https://doi.org/10.1111/cgf.70152","url":null,"abstract":"<div>\u0000 <p>Revealing relevant information on demand is an essential requirement for visual data exploration. In this state-of-the-art report, we review and classify techniques that are inspired by the physical metaphor of un/folding to reveal relevant information or, conversely, to reduce irrelevant information in data visualizations. Similar to focus+context approaches, un/foldable visualizations transform the visual data representation, often between different granularities, in an integrated manner while preserving the overall context. This typically involves switching between different visibility states of data elements or adjusting the graphical abstraction linked by gradual display transitions. We analyze a literature corpus of 101 visualization techniques specifically with respect to their use of the un/folding metaphor. In particular, we consider the type of data, the focus scope and the effect scope, the number of un/folding states, the transformation type, and the controllability and interaction directness of un/folding. The collection of un/foldables is available as an online catalog that includes classic focus+context, semantic zooming, and multi-scale visualizations as well as contemporary un/foldable visualizations. From our literature analysis, we further extract families of un/folding techniques, summarize empirical findings to date, and identify promising research directions for un/foldable data visualization.</p>\u0000 </div>","PeriodicalId":10687,"journal":{"name":"Computer Graphics Forum","volume":"44 3","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cgf.70152","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144681634","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}

{"title":"Nodes, Edges, and Artistic Wedges: A Survey on Network Visualization in Art History","authors":"Michaela Tuscher,&nbsp;Velitchko Filipov,&nbsp;Teresa Kamencek,&nbsp;Raphael Rosenberg,&nbsp;Silvia Miksch","doi":"10.1111/cgf.70154","DOIUrl":"https://doi.org/10.1111/cgf.70154","url":null,"abstract":"<div>\u0000 \u0000 <p>Art history traditionally relies on qualitative methods. However, the increasing availability of digitized archives has opened new possibilities for research by integrating visual analytics. This survey presents a comprehensive review of the intersection between art history and visual analytics, focusing on network visualization and how it supports researchers in analyzing and understanding complex art historical relationships through <b>nodes</b> (e.g., artists, artworks, institutions) and <b>edges</b> (the relationships between them). We explore how these approaches enable dynamic analysis, offering novel perspectives on artistic influence, stylistic evolution, and social interactions within the art world. Through this, we also examine <b>wedges</b>, a metaphor for the friction often present in art history between individuals and institutions. These tensions, which have historically played a pivotal role in shaping artistic movements, are now better understood through the lens of network visualization, revealing how conflicts and power dynamics influenced the development of art. Through a hierarchical categorization of the literature, we outline saturated problems and research areas as well as ongoing challenges in art historical research. Furthermore, we highlight the potential of visual analytics to bridge the gap between traditional qualitative research and modern computational analysis, offering interactive exploration, temporal analysis, and complex network visualization. We provide a structured foundation for future research in art history, emphasizing the value of network visualization in enriching the understanding of art history.</p>\u0000 </div>","PeriodicalId":10687,"journal":{"name":"Computer Graphics Forum","volume":"44 3","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cgf.70154","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144681615","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}

{"title":"Uncertainty-Aware Visualization of Biomolecular Structures","authors":"A. Sterzik,&nbsp;C. Gillmann,&nbsp;M. Krone,&nbsp;K. Lawonn","doi":"10.1111/cgf.70155","DOIUrl":"https://doi.org/10.1111/cgf.70155","url":null,"abstract":"<p>Molecular structure visualization is fundamental to molecular biology, aiding in understanding complex biological processes. While advancements in molecular visualization have greatly improved the representation of these structures, inherent uncertainties—such as inaccuracies in atomic positions or variability in secondary structure classifications—impact the accuracy of the visualizations. Uncertainty-aware visualization (UAV) emerged as a response to these challenges, integrating uncertainty into visual representations to improve data interpretation and decision-making. Despite extensive work on both molecular and uncertainty visualization (UV), there is a lack of comprehensive surveys addressing the intersection of these two fields. This paper provides a state-of-the-art review of UAV approaches for biomolecular structures. We propose a classification schema that organizes existing methods based on the type of molecule visualized, the manifestation of uncertainty, and the mapping of uncertainty to a visual representation. Using this framework, we identified research gaps and areas for future exploration in uncertainty-aware biomolecular structure visualization.</p><p>The visualization of molecular structures has long been an integral part of structural molecular biology and continues to develop as a critical tool in scientific and industrial research. Since the early days of the field, molecular visualization has enabled researchers to gain an intuitive understanding of complex molecular systems that would otherwise be inaccessible through raw numerical data alone. Structural representations provide insight into the form and function of molecules, which are inherently linked. A well-known example is the lock-and-key metaphor for molecular docking, which, while simplified, illustrates the importance of molecular shape in determining biological interactions. By transforming abstract data into interpretable visual models, visualization allows scientists to identify relationships, mechanisms, and patterns essential for advancing molecular biology, drug design, and bioinformatics.</p><p>Levinthal [<span>Lev66</span>] was the first to use known positions of atoms, obtained by early X-Ray diffraction studies [<span>Ken61</span>, <span>Per64</span>], to interactively visualize 3D structural images of small proteins. Over the years, molecular visualization has advanced significantly. The field has seen the development of diverse visualization and visual analysis techniques, driven by growing computational capabilities and the increasing complexity of biological data. This progress is reflected in comprehensive surveys such as the one by Kozlíková et al. [<span>KKF*17</span>], which provides a detailed overview of the state-of-the-art in molecular structure visualization.</p><p>One of the inherent challenges with visualization is the potential introduction of uncertainty at any stage of the visualization pipeline, from data acquisition to the final v","PeriodicalId":10687,"journal":{"name":"Computer Graphics Forum","volume":"44 3","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cgf.70155","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144681616","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}

{"title":"Random Access Segmentation Volume Compression for Interactive Volume Rendering","authors":"M. Piochowiak,&nbsp;F. Kurpicz,&nbsp;C. Dachsbacher","doi":"10.1111/cgf.70116","DOIUrl":"https://doi.org/10.1111/cgf.70116","url":null,"abstract":"<div>\u0000 <p>Segmentation volumes are voxel data sets often used in machine learning, connectomics, and natural sciences. Their large sizes make compression indispensable for storage and processing, including GPU video memory constrained real-time visualization. Fast Compressed Segmentation Volumes (CSGV) [PD24] provide strong brick-wise compression and random access at the brick level. Voxels within a brick, however, have to be decoded serially and thus rendering requires caching of visible full bricks, consuming extra memory. Without caching, accessing voxels can have a worst-case decoding overhead of up to a full brick (typically over 32.000 voxels). We present CSGV-R which provide true multi-resolution random access on a per-voxel level. We leverage Huffman-shaped Wavelet Trees for random accesses to variable bit-length encoding and their rank operation to query label palette offsets in bricks. Our real-time segmentation volume visualization removes decoding artifacts from CSGV and renders CSGV-R volumes without caching bricks at faster render times. CSGV-R has slightly lower compression rates than CSGV, but outperforms Neuroglancer, the state-of-the-art compression technique with true random access, with <i>2×</i> to <i>4×</i> smaller data sets at rates between <i>0.648%</i> and <i>4.411%</i> of the original volume sizes.</p>\u0000 </div>","PeriodicalId":10687,"journal":{"name":"Computer Graphics Forum","volume":"44 3","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cgf.70116","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144681617","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}