Volume 44 (2025)

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BI‐LAVA: Biocuration With Hierarchical Image Labelling Through Active Learning and Visual Analytics
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2024) Trelles, Juan; Wentzel, Andrew; Berrios, William; Shatkay, Hagit; Marai, G. Elisabeta
In the biomedical domain, taxonomies organize the acquisition modalities of scientific images in hierarchical structures. Such taxonomies leverage large sets of correct image labels and provide essential information about the importance of a scientific publication, which could then be used in biocuration tasks. However, the hierarchical nature of the labels, the overhead of processing images, the absence or incompleteness of labelled data and the expertise required to label this type of data impede the creation of useful datasets for biocuration. From a multi‐year collaboration with biocurators and text‐mining researchers, we derive an iterative visual analytics and active learning (AL) strategy to address these challenges. We implement this strategy in a system called BI‐LAVA—Biocuration with Hierarchical Image Labelling through Active Learning and Visual Analytics. BI‐LAVA leverages a small set of image labels, a hierarchical set of image classifiers and AL to help model builders deal with incomplete ground‐truth labels, target a hierarchical taxonomy of image modalities and classify a large pool of unlabelled images. BI‐LAVA's front end uses custom encodings to represent data distributions, taxonomies, image projections and neighbourhoods of image thumbnails, which help model builders explore an unfamiliar image dataset and taxonomy and correct and generate labels. An evaluation with machine learning practitioners shows that our mixed human–machine approach successfully supports domain experts in understanding the characteristics of classes within the taxonomy, as well as validating and improving data quality in labelled and unlabelled collections.
Automatic Inbetweening for Stroke‐Based Painterly Animation
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2024) Barroso, Nicolas; Fondevilla, Amélie; Vanderhaeghe, David
Painterly 2D animation, like the paint‐on‐glass technique, is a tedious task performed by skilled artists, primarily using traditional manual methods. Although CG tools can simplify the creation process, previous works often focus on temporal coherence, which typically results in the loss of the handmade look and feel. In contrast to cartoon animation, where regions are typically filled with smooth gradients, stroke‐based stylized 2D animation requires careful consideration of how shapes are filled, as each stroke may be perceived individually. We propose a method to generate intermediate frames using example keyframes and a motion description. This method allows artists to create only one image for every five to 10 output images in the animation, while the automatically generated intermediate frames provide plausible inbetween frames.
Conditional Font Generation With Content Pre‐Train and Style Filter
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2024) Hong, Yang; Li, Yinfei; Qiao, Xiaojun; Zhang, Junsong
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.
MoNeRF: Deformable Neural Rendering for Talking Heads via Latent Motion Navigation
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2024) Li, X.; Ding, Y.; Li, R.; Tang, Z.; Li, K.
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.
Dynamic Voxel‐Based Global Illumination
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2024) Cosin Ayerbe, Alejandro; Poulin, Pierre; Patow, Gustavo
Global illumination computation in real time has been an objective for Computer Graphics since its inception. Unfortunately, its implementation has challenged up to now the most advanced hardware and software solutions. We propose a real‐time voxel‐based global illumination solution for a single light bounce that handles static and dynamic objects with diffuse materials under a dynamic light source. The combination of ray tracing and voxelization on the GPU offers scalability and performance. Our divide‐and‐win approach, which ray traces separately static and dynamic objects, reduces the re‐computation load with updates of any number of dynamic objects. Our results demonstrate the effectiveness of our approach, allowing the real‐time display of global illumination effects, including colour bleeding and indirect shadows, for complex scenes containing millions of polygons.
Natural Language Generation for Visualizations: State of the Art, Challenges and Future Directions
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2024) Hoque, E.; Islam, M. Saidul
Natural language and visualization are two complementary modalities of human communication that play a crucial role in conveying information effectively. While visualizations help people discover trends, patterns and anomalies in data, natural language descriptions help explain these insights. Thus, combining text with visualizations is a prevalent technique for effectively delivering the core message of the data. Given the rise of natural language generation (NLG), there is a growing interest in automatically creating natural language descriptions for visualizations, which can be used as chart captions, answering questions about charts or telling data‐driven stories. In this survey, we systematically review the state of the art on NLG for visualizations and introduce a taxonomy of the problem. The NLG tasks fall within the domain of natural language interfaces (NLIs) for visualization, an area that has garnered significant attention from both the research community and industry. To narrow down the scope of the survey, we primarily concentrate on the research works that focus on text generation for visualizations. To characterize the NLG problem and the design space of proposed solutions, we pose five Wh‐questions, why and how NLG tasks are performed for visualizations, what the task inputs and outputs are, as well as where and when the generated texts are integrated with visualizations. We categorize the solutions used in the surveyed papers based on these ‘five Wh‐questions’. Finally, we discuss the key challenges and potential avenues for future research in this domain.
Continuous Toolpath Optimization for Simultaneous Four‐Axis Subtractive Manufacturing
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2024) Zhang, Zhenmin; Shi, Zihan; Zhong, Fanchao; Zhang, Kun; Zhang, Wenjing; Guo, Jianwei; Tu, Changhe; Zhao, Haisen
Simultaneous four‐axis machining involves a cutter that moves in all degrees of freedom during carving. This strategy provides higher‐quality surface finishing compared to positional machining. However, it has not been well‐studied in research. In this study, we propose the first end‐to‐end computational framework to optimize the toolpath for fabricating complex models using simultaneous four‐axis subtractive manufacturing. In our technique, we first slice the input 3D model into uniformly distributed 2D layers. For each slicing layer, we perform an accessibility analysis for each intersected contour within this layer. Then, we proceed with over‐segmentation and a bottom‐up connecting process to generate a minimal number of fabricable segments. Finally, we propose post‐processing techniques to further optimize the tool directionand the transfer path between segments. Physical experiments of nine models demonstrate our significant improvements in both fabrication quality and efficiency, compared to the positional strategy and two simultaneous tool paths generated by industry‐standard CAM systems.
Mesh Simplification for Unfolding*
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2024) Bhargava, M.; Schreck, C.; Freire, M.; Hugron, P. A.; Lefebvre, S.; Sellán, S.; Bickel, B.
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.
Deep‐Learning‐Based Facial Retargeting Using Local Patches
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2024) Choi, Yeonsoo; Lee, Inyup; Cha, Sihun; Kim, Seonghyeon; Jung, Sunjin; Noh, Junyong
In the era of digital animation, the quest to produce lifelike facial animations for virtual characters has led to the development of various retargeting methods. While the retargeting facial motion between models of similar shapes has been very successful, challenges arise when the retargeting is performed on stylized or exaggerated 3D characters that deviate significantly from human facial structures. In this scenario, it is important to consider the target character's facial structure and possible range of motion to preserve the semantics assumed by the original facial motions after the retargeting. To achieve this, we propose a local patch‐based retargeting method that transfers facial animations captured in a source performance video to a target stylized 3D character. Our method consists of three modules. The Automatic Patch Extraction Module extracts local patches from the source video frame. These patches are processed through the Reenactment Module to generate correspondingly re‐enacted target local patches. The Weight Estimation Module calculates the animation parameters for the target character at every frame for the creation of a complete facial animation sequence. Extensive experiments demonstrate that our method can successfully transfer the semantic meaning of source facial expressions to stylized characters with considerable variations in facial feature proportion.
ConAn: Measuring and Evaluating User Confidence in Visual Data Analysis Under Uncertainty
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2024) Musleh, M.; Ceneda, D.; Ehlers, H.; Raidou, R. G.
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.
Detecting, Interpreting and Modifying the Heterogeneous Causal Network in Multi‐Source Event Sequences
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2024) Xu, Shaobin; Sun, Minghui
Uncovering causal relations from event sequences to guide decision‐making has become an essential task across various domains. Unfortunately, this task remains a challenge because real‐world event sequences are usually collected from multiple sources. Most existing works are specifically designed for homogeneous causal analysis between events from a single source, without considering cross‐source causality. In this work, we propose a heterogeneous causal analysis algorithm to detect the heterogeneous causal network between high‐level events in multi‐source event sequences while preserving the causal semantic relationships between diverse data sources. Additionally, the flexibility of our algorithm allows to incorporate high‐level event similarity into learning model and provides a fuzzy modification mechanism. Based on the algorithm, we further propose a visual analytics framework that supports interpreting the causal network at three granularities and offers a multi‐granularity modification mechanism to incorporate user feedback efficiently. We evaluate the accuracy of our algorithm through an experimental study, illustrate the usefulness of our system through a case study, and demonstrate the efficiency of our modification mechanisms through a user study.
A Generative Adversarial Network for Upsampling of Direct Volume Rendering Images
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2024) Jin, Ge; Jung, Younhyun; Fulham, Michael; Feng, Dagan; Kim, Jinman
Direct volume rendering (DVR) is an important tool for scientific and medical imaging visualization. Modern GPU acceleration has made DVR more accessible; however, the production of high‐quality rendered images with high frame rates is computationally expensive. We propose a deep learning method with a reduced computational demand. We leveraged a conditional generative adversarial network (cGAN) to upsample DVR images (a rendered scene), with a reduced sampling rate to obtain similar visual quality to that of a fully sampled method. Our dvrGAN is combined with a colour‐based loss function that is optimized for DVR images where different structures such as skin, bone, . are distinguished by assigning them distinct colours. The loss function highlights the structural differences between images, by examining pixel‐level colour, and thus helps identify, for instance, small bones in the limbs that may not be evident with reduced sampling rates. We evaluated our method in DVR of human computed tomography (CT) and CT angiography (CTA) volumes. Our method retained image quality and reduced computation time when compared to fully sampled methods and outperformed existing state‐of‐the‐art upsampling methods.
Stress‐Aligned Hexahedral Lattice Structures
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2024) Bukenberger, D. R.; Wang, J.; Wu, J.; Westermann, R.
Maintaining the maximum stiffness of components with as little material as possible is an overarching objective in computational design and engineering. It is well‐established that in stiffness‐optimal designs, material is aligned with orthogonal principal stress directions. In the limit of material volume, this alignment forms micro‐structures resembling quads or hexahedra. Achieving a globally consistent layout of such orthogonal micro‐structures presents a significant challenge, particularly in three‐dimensional settings. In this paper, we propose a novel geometric algorithm for compiling stress‐aligned hexahedral lattice structures. Our method involves deforming an input mesh under load to align the resulting stress field along an orthogonal basis. The deformed object is filled with a hexahedral grid, and the deformation is reverted to recover the original shape. The resulting stress‐aligned mesh is used as basis for a final hollowing procedure, generating a volume‐reduced stiff infill composed of hexahedral micro‐structures. We perform quantitative comparisons with structural optimization and hexahedral meshing approaches and demonstrate the superior mechanical performance of our designs with finite element simulation experiments.
HPSCAN: Human Perception‐Based Scattered Data Clustering
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2024) Hartwig, S.; Onzenoodt, C. v.; Engel, D.; Hermosilla, P.; Ropinski, T.
Cluster separation is a task typically tackled by widely used clustering techniques, such as k‐means or DBSCAN. However, these algorithms are based on non‐perceptual metrics, and our experiments demonstrate that their output does not reflect human cluster perception. To bridge the gap between human cluster perception and machine‐computed clusters, we propose HPSCAN, a learning strategy that operates directly on scattered data. To learn perceptual cluster separation on such data, we crowdsourced the labeling of bivariate (scatterplot) datasets to 384 human participants. We train our HPSCAN model on these human‐annotated data. Instead of rendering these data as scatterplot images, we used their and point coordinates as input to a modified PointNet++ architecture, enabling direct inference on point clouds. In this work, we provide details on how we collected our dataset, report statistics of the resulting annotations, and investigate the perceptual agreement of cluster separation for real‐world data. We also report the training and evaluation protocol for HPSCAN and introduce a novel metric, that measures the accuracy between a clustering technique and a group of human annotators. We explore predicting point‐wise human agreement to detect ambiguities. Finally, we compare our approach to 10 established clustering techniques and demonstrate that HPSCAN is capable of generalizing to unseen and out‐of‐scope data.
Light Distribution Models for Tree Growth Simulation
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2024) Nauber, Tristan; Mäder, Patrick
The simulation and modelling of tree growth is a complex subject with a long history and an important area of research in both computer graphics and botany. For more than 50 years, new approaches to this topic have been presented frequently, including several aspects to increase realism. To further improve these achievements, we present a compact and robust functional‐structural plant model (FSPM) that is consistent with botanical rules. While we show several extensions to typical approaches, we focus mainly on the distribution of light as a resource in three‐dimensional space. We therefore present four different light distribution models based on ray tracing, space colonization, voxel‐based approaches and bounding volumes. By simulating individual light sources, we were able to create a more specified scene setup for plant simulation than it has been presented in the past. By taking into account such a more accurate distribution of light in the environment, this technique is capable of modelling realistic and diverse tree models.
Generalized Lipschitz Tracing of Implicit Surfaces
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2024) Bán, Róbert; Valasek, Gábor
We present a versatile and robust framework to render implicit surfaces defined by black‐box functions that only provide function value queries. We assume that the input function is locally Lipschitz continuous; however, we presume no prior knowledge of its Lipschitz constants. Our pre‐processing step generates a discrete acceleration structure, a Lipschitz field, that provides data to infer local and directional Lipschitz upper bounds. These bounds are used to compute safe step sizes along rays during rendering. The Lipschitz field is constructed by generating local polynomial approximations to the input function, then bounding the derivatives of the approximating polynomials. The accuracy of the approximation is controlled by the polynomial degree and the granularity of the spatial resolution used during fitting, which is independent from the resolution of the Lipschitz field. We demonstrate that our process can be implemented in a massively parallel way, enabling straightforward integration into interactive and real‐time modelling workflows. Since the construction only requires function value evaluations, the input surface may be represented either procedurally or as an arbitrarily filtered grid of function samples. We query the original implicit representation upon ray trace, as such, we preserve the geometric and topological details of the input as long as the Lipschitz field supplies conservative estimates. We demonstrate our method on both procedural and discrete implicit surfaces and compare its exact and approximate variants.
Efficient Environment Map Rendering Based on Decomposition
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2024) Wu, Yu‐Ting
This paper presents an efficient environment map sampling algorithm designed to render high‐quality, low‐noise images with only a few light samples, making it ideal for real‐time applications. We observe that bright pixels in the environment map produce high‐frequency shading effects, such as sharp shadows and shading, while the rest influence the overall tone of the scene. Building on this insight, our approach differs from existing techniques by categorizing the pixels in an environment map into emissive and non‐emissive regions and developing specialized algorithms tailored to the distinct properties of each region. By decomposing the environment lighting, we ensure that light sources are deposited on bright pixels, leading to more accurate shadows and specular highlights. Additionally, this strategy allows us to exploit the smoothness in the low‐frequency component by rendering a smaller image with more lights, thereby enhancing shading accuracy. Extensive experiments demonstrate that our method significantly reduces shadow artefacts and image noise compared to previous techniques, while also achieving lower numerical errors across a range of illumination types, particularly under limited sample conditions.
The State of the Art in User‐Adaptive Visualizations
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2024) Yanez, Fernando; Conati, Cristina; Ottley, Alvitta; Nobre, Carolina
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 Ⓐ from the user and any relevant peripheral information. Perform computational Ⓑ with this input to construct a Ⓒ . Employ Ⓓ logic to identify when and how to introduce Ⓔ . 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.
A Particle‐Based Approach to Extract Dynamic 3D FTLE Ridge Geometry
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2024) Stelter, Daniel; Wilde, Thomas; Rössl, Christian; Theisel, Holger
Lagrangian coherent structures (LCS) is an important concept for the visualization of unsteady flows. They describe the boundaries of regions for which material transport stays mostly coherent over time which can help for a better understanding of dynamical systems. One of the most common techniques for their computation is the extraction of ridges from the finite‐time Lyapunov exponent (FTLE) field. FTLE ridges are challenging to extract, both in terms of accuracy and performance, because they expose strong gradients of the underlying field, tend to come close to each other and are dynamic with respect to different time parameters. We present a new method for extracting FTLE ridges for series of integration times which is able to show how coherent regions and their borders evolve over time. Our techniques mainly build on a particle system which is used for sampling the ridges uniformly. This system is highly optimized for the challenges of FTLE ridge extraction. Further, it is able to take advantage of the continuous evolvement of the ridges which makes their sampling for multiple integration times much faster. We test our method on multiple 3D datasets and compare it to the standard Marching Ridges technique. For the extraction examples our method is 13 to over 300 times faster, suggesting a significant advantage.
DeepFracture: A Generative Approach for Predicting Brittle Fractures with Neural Discrete Representation Learning
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2025) Huang, Yuhang; Kanai, Takashi
In the field of brittle fracture animation, generating realistic destruction animations using physics‐based simulation methods is computationally expensive. While techniques based on Voronoi diagrams or pre‐fractured patterns are effective for real‐time applications, they fail to incorporate collision conditions when determining fractured shapes during runtime. This paper introduces a novel learning‐based approach for predicting fractured shapes based on collision dynamics at runtime. Our approach seamlessly integrates realistic brittle fracture animations with rigid body simulations, utilising boundary element method (BEM) brittle fracture simulations to generate training data. To integrate collision scenarios and fractured shapes into a deep learning framework, we introduce generative geometric segmentation, distinct from both instance and semantic segmentation, to represent 3D fragment shapes. We propose an eight‐dimensional latent code to address the challenge of optimising multiple discrete fracture pattern targets that share similar continuous collision latent codes. This code will follow a discrete normal distribution corresponding to a specific fracture pattern within our latent impulse representation design. This adaptation enables the prediction of fractured shapes using neural discrete representation learning. Our experimental results show that our approach generates considerably more detailed brittle fractures than existing techniques, while the computational time is typically reduced compared to traditional simulation methods at comparable resolutions.

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