Volume 44 (2025)

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2D Neural Fields with Learned Discontinuities
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Liu, Chenxi; Wang, Siqi; Fisher, Matthew; Aneja, Deepali; Jacobson, Alec; Bousseau, Adrien; Day, Angela
Effective representation of 2D images is fundamental in digital image processing, where traditional methods like raster and vector graphics struggle with sharpness and textural complexity, respectively. Current neural fields offer high fidelity and resolution independence but require predefined meshes with known discontinuities, restricting their utility. We observe that by treating all mesh edges as potential discontinuities, we can represent the discontinuity magnitudes as continuous variables and optimize. We further introduce a novel discontinuous neural field model that jointly approximates the target image and recovers discontinuities. Through systematic evaluations, our neural field outperforms other methods that fit unknown discontinuities with discontinuous representations, exceeding Field of Junction and Boundary Attention by over 11dB in both denoising and super-resolution tasks and achieving 3.5× smaller Chamfer distances than Mumford-Shah-based methods. It also surpasses InstantNGP with improvements of more than 5dB (denoising) and 10dB (super-resolution). Additionally, our approach shows remarkable capability in approximating complex artistic and natural images and cleaning up diffusion-generated depth maps.
4-LEGS: 4D Language Embedded Gaussian Splatting
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Fiebelman, Gal; Cohen, Tamir; Morgenstern, Ayellet; Hedman, Peter; Averbuch-Elor, Hadar; Bousseau, Adrien; Day, Angela
The emergence of neural representations has revolutionized our means for digitally viewing a wide range of 3D scenes, enabling the synthesis of photorealistic images rendered from novel views. Recently, several techniques have been proposed for connecting these low-level representations with the high-level semantics understanding embodied within the scene. These methods elevate the rich semantic understanding from 2D imagery to 3D representations, distilling high-dimensional spatial features onto 3D space. In our work, we are interested in connecting language with a dynamic modeling of the world. We show how to lift spatio-temporal features to a 4D representation based on 3D Gaussian Splatting. This enables an interactive interface where the user can spatiotemporally localize events in the video from text prompts. We demonstrate our system on public 3D video datasets of people and animals performing various actions.
Accessible Text Descriptions for UpSet Plots
(The Eurographics Association and John Wiley & Sons Ltd., 2025) McNutt, Andrew; McCracken, Maggie K.; Eliza, Ishrat Jahan; Hajas, Daniel; Wagoner, Jake; Lanza, Nate; Wilburn, Jack; Creem-Regehr, Sarah; Lex, Alexander; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
Data visualizations are typically not accessible to blind and low-vision (BLV) users. Automatically generating text descriptions offers an enticing mechanism for democratizing access to the information held in complex scientific charts, yet appropriate procedures for generating those texts remain elusive. Pursuing this issue, we study a single complex chart form: UpSet plots. UpSet Plots are a common way to analyze set data, an area largely unexplored by prior accessibility literature. By analyzing the patterns present in real-world examples, we develop a system for automatically captioning any UpSet plot. We evaluated the utility of our captions via semi-structured interviews with (N=11) BLV users and found that BLV users find them informative. In extensions, we find that sighted users can use our texts similarly to UpSet plots and that they are better than naive LLM usage.
Adaptive Multi-view Radiance Caching for Heterogeneous Participating Media
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Stadlbauer, Pascal; Tatzgern, Wolfgang; Mueller, Joerg H.; Winter, Martin; Stojanovic, Robert; Weinrauch, Alexander; Steinberger, Markus; Bousseau, Adrien; Day, Angela
Achieving lifelike atmospheric effects, such as fog, is essential in creating immersive environments and poses a formidable challenge in real-time rendering. Highly realistic rendering of complex lighting interacting with dynamic fog can be very resourceintensive, due to light bouncing through a complex participating media multiple times. We propose an approach that uses a multi-layered spherical harmonics probe grid to share computations temporarily. In addition, this world-space storage enables the sharing of radiance data between multiple viewers. In the context of cloud rendering this means faster rendering and a significant enhancement in overall rendering quality with efficient resource utilization.
All-frequency Full-body Human Image Relighting
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Tajima, Daichi; Kanamori, Yoshihiro; Endo, Yuki; Bousseau, Adrien; Day, Angela
Relighting of human images enables post-photography editing of lighting effects in portraits. The current mainstream approach uses neural networks to approximate lighting effects without explicitly accounting for the principle of physical shading. As a result, it often has difficulty representing high-frequency shadows and shading. In this paper, we propose a two-stage relighting method that can reproduce physically-based shadows and shading from low to high frequencies. The key idea is to approximate an environment light source with a set of a fixed number of area light sources. The first stage employs supervised inverse rendering from a single image using neural networks and calculates physically-based shading. The second stage then calculates shadow for each area light and sums up to render the final image. We propose to make soft shadow mapping differentiable for the area-light approximation of environment lighting. We demonstrate that our method can plausibly reproduce all-frequency shadows and shading caused by environment illumination, which have been difficult to reproduce using existing methods.
Approximating Procedural Models of 3D Shapes with Neural Networks
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Hossain, Ishtiaque; Shen, I-Chao; Kaick, Oliver van; Bousseau, Adrien; Day, Angela
Procedural modeling is a popular technique for 3D content creation and offers a number of advantages over alternative techniques for modeling 3D shapes. However, given a procedural model, predicting the procedural parameters of existing data provided in different modalities can be challenging. This is because the data may be in a different representation than the one generated by the procedural model, and procedural models are usually not invertible, nor are they differentiable. In this paper, we address these limitations and introduce an invertible and differentiable representation for procedural models. We approximate parameterized procedures with a neural network architecture NNProc that learns both the forward and inverse mapping of the procedural model by aligning the latent spaces of shape parameters and shapes. The network is trained in a manner that is agnostic to the inner workings of the procedural model, implying that models implemented in different languages or systems can be used. We demonstrate how the proposed representation can be used for both forward and inverse procedural modeling. Moreover, we show how NNProc can be used in conjunction with optimization for applications such as shape reconstruction from an image or a 3D Gaussian Splatting.
ASMR: Adaptive Skeleton-Mesh Rigging and Skinning via 2D Generative Prior
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Hong, Seokhyeon; Choi, Soojin; Kim, Chaelin; Cha, Sihun; Noh, Junyong; Bousseau, Adrien; Day, Angela
Despite the growing accessibility of skeletal motion data, integrating it for animating character meshes remains challenging due to diverse configurations of both skeletons and meshes. Specifically, the body scale and bone lengths of the skeleton should be adjusted in accordance with the size and proportions of the mesh, ensuring that all joints are accurately positioned within the character mesh. Furthermore, defining skinning weights is complicated by variations in skeletal configurations, such as the number of joints and their hierarchy, as well as differences in mesh configurations, including their connectivity and shapes. While existing approaches have made efforts to automate this process, they hardly address the variations in both skeletal and mesh configurations. In this paper, we present a novel method for the automatic rigging and skinning of character meshes using skeletal motion data, accommodating arbitrary configurations of both meshes and skeletons. The proposed method predicts the optimal skeleton aligned with the size and proportion of the mesh as well as defines skinning weights for various meshskeleton configurations, without requiring explicit supervision tailored to each of them. By incorporating Diffusion 3D Features (Diff3F) as semantic descriptors of character meshes, our method achieves robust generalization across different configurations. To assess the performance of our method in comparison to existing approaches, we conducted comprehensive evaluations encompassing both quantitative and qualitative analyses, specifically examining the predicted skeletons, skinning weights, and deformation quality.
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.
Axis-Normalized Ray-Box Intersection
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Friederichs, Fabian; Benthin, Carsten; Grogorick, Steve; Eisemann, Elmar; Magnor, Marcus; Eisemann, Martin; Bousseau, Adrien; Day, Angela
Ray-axis aligned bounding box intersection tests play a crucial role in the runtime performance of many rendering applications, driven not by complexity but mainly by the volume of tests required. While existing solutions were believed to be pretty much optimal in terms of runtime on current hardware, our paper introduces a new intersection test requiring fewer arithmetic operations compared to all previous methods. By transforming the ray we eliminate the need for one third of the traditional bounding-slab tests and achieve a speed enhancement of approximately 13.8% or 10.9%, depending on the compiler.We present detailed runtime analyses in various scenarios.
Benchmarking Visual Language Models on Standardized Visualization Literacy Tests
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Pandey, Saugat; Ottley, Alvitta; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
The increasing integration of Visual Language Models (VLMs) into visualization systems demands a comprehensive understanding of their visual interpretation capabilities and constraints. While existing research has examined individual models, systematic comparisons of VLMs' visualization literacy remain unexplored. We bridge this gap through a rigorous, first-ofits- kind evaluation of four leading VLMs (GPT-4, Claude, Gemini, and Llama) using standardized assessments: the Visualization Literacy Assessment Test (VLAT) and Critical Thinking Assessment for Literacy in Visualizations (CALVI). Our methodology uniquely combines randomized trials with structured prompting techniques to control for order effects and response variability - a critical consideration overlooked in many VLM evaluations. Our analysis reveals that while specific models demonstrate competence in basic chart interpretation (Claude achieving 67.9% accuracy on VLAT), all models exhibit substantial difficulties in identifying misleading visualization elements (maximum 30.0% accuracy on CALVI). We uncover distinct performance patterns: strong capabilities in interpreting conventional charts like line charts (76-96% accuracy) and detecting hierarchical structures (80-100% accuracy), but consistent difficulties with data-dense visualizations involving multiple encodings (bubble charts: 18.6-61.4%) and anomaly detection (25-30% accuracy). Significantly, we observe distinct uncertainty management behavior across models, with Gemini displaying heightened caution (22.5% question omission) compared to others (7-8%). These findings provide crucial insights for the visualization community by establishing reliable VLM evaluation benchmarks, identifying areas where current models fall short, and highlighting the need for targeted improvements in VLM architectures for visualization tasks. To promote reproducibility, encourage further research, and facilitate benchmarking of future VLMs, our complete evaluation framework, including code, prompts, and analysis scripts, is available at https://github.com/washuvis/VisLit-VLM-Eval.
Beyond Entertainment: An Investigation of Externalization Design in Video Games
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Becker, Franziska; Warnking, Rene Pascal; Brückler, Hendrik; Blascheck, Tanja; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
This article investigates when and how video games enable players to create externalizations in a diverse sample of 388 video games. We follow a grounded-theory approach, extracting externalizations from video games to explore design ideas and relate them to practices in visualization. Video games often engage players in problem-solving activities, like solving a murder mystery or optimizing a strategy, requiring players to interpret heterogeneous data-much like tasks in the visualization domain. In many cases, externalizations can help reduce a user's mental load by making tangible what otherwise only lives in their head, acting as external storage or a visual playground. Over five coding phases, we created a hierarchy of 277 tags to describe the video games in our collection, from which we extracted 169 externalizations. We characterize these externalizations along nine dimensions like mental load, visual encodings, and motivations, resulting in 13 categories divided into four clusters: quick access, storage, sensemaking, and communication. We formulate considerations to guide future work, looking at tasks and challenges, naming potentials for inspiration, and discussing which topics could advance the state of externalization.
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.
BlendSim: Simulation on Parametric Blendshapes using Spacetime Projective Dynamics
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Wu, Yuhan; Umetani, Nobuyuki; Bousseau, Adrien; Day, Angela
We propose BlendSim, a novel framework for editable simulation using spacetime optimization on the lightweight animation representation. Traditional spacetime control methods suffer from a high computational complexity, which limits their use in interactive animation. The proposed approach effectively reduces the dimensionality of the problem by representing the motion trajectories of each vertex using continuous parametric Bézier splines with variable keyframe times. Because this mesh animation representation is continuous and fully differentiable, it can be optimized such that it follows the laws of physics under various constraints. The proposed method also integrates constraints, such as collisions and cyclic motion, making it suitable for real-world applications where seamless looping and physical interactions are required. Leveraging projective dynamics, we further enhance the computational efficiency by decoupling the optimization into local parallelizable and global quadratic steps, enabling a fast and stable simulation. In addition, BlendSim is compatible with modern animation workflows and file formats, such as the glTF, making it practical way for authoring and transferring mesh animation.
Bracket Diffusion: HDR Image Generation by Consistent LDR Denoising
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Bemana, Mojtaba; Leimkühler, Thomas; Myszkowski, Karol; Seidel, Hans-Peter; Ritschel, Tobias; Bousseau, Adrien; Day, Angela
We demonstrate generating HDR images using the concerted action of multiple black-box, pre-trained LDR image diffusion models. Common diffusion models are not HDR as, first, there is no sufficiently large HDR image dataset available to re-train them, and, second, even if it was, re-training such models is impossible for most compute budgets. Instead, we seek inspiration from the HDR image capture literature that traditionally fuses sets of LDR images, called ''exposure brackets'', to produce a single HDR image. We operate multiple denoising processes to generate multiple LDR brackets that together form a valid HDR result. To this end, we introduce a brackets consistency term into the diffusion process to couple the brackets such that they agree across the exposure range they share. We demonstrate HDR versions of state-of-the-art unconditional and conditional as well as restoration-type (LDR2HDR) generative modeling.
CEDRL: Simulating Diverse Crowds with Example-Driven Deep Reinforcement Learning
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Panayiotou, Andreas; Aristidou, Andreas; Charalambous, Panayiotis; Bousseau, Adrien; Day, Angela
The level of realism in virtual crowds is strongly affected by the presence of diverse crowd behaviors. In real life, we can observe various scenarios, ranging from pedestrians moving on a shopping street, people talking in static groups, or wandering around in a public park. Most of the existing systems optimize for specific behaviors such as goal-seeking and collision avoidance, neglecting to consider other complex behaviors that are usually challenging to capture or define. Departing from the conventional use of Supervised Learning, which requires vast amounts of labeled data and often lacks controllability, we introduce Crowds using Example-driven Deep Reinforcement Learning (CEDRL), a framework that simultaneously leverages multiple crowd datasets to model a broad spectrum of human behaviors. This approach enables agents to adaptively learn and exhibit diverse behaviors, enhancing their ability to generalize decisions across unseen states. The model can be applied to populate novel virtual environments while providing real-time controllability over the agents' behaviors. We achieve this through the design of a reward function aligned with real-world observations and by employing curriculum learning that gradually diminishes the agents' observation space. A complexity characterization metric defines each agent's high-level crowd behavior, linking it to the agent's state and serving as an input to the policy network. Additionally, a parametric reward function, influenced by the type of crowd task, facilitates the learning of a diverse and abstract behavior ''skill'' set. We evaluate our model on both training and unseen real-world data, comparing against other simulators, showing its ability to generalize across scenarios and accurately reflect the observed complexity of behaviors. We also examine our system's controllability by adjusting the complexity weight, discovering that higher values lead to more complex behaviors such as wandering, static interactions, and group dynamics like joining or leaving. Finally, we demonstrate our model's capabilities in novel synthetic scenarios.
Cloth Animation with Time-dependent Persistent Wrinkles
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Gong, Deshan; Yang, Yin; Shao, Tianjia; Wang, He; Bousseau, Adrien; Day, Angela
Persistent wrinkles are often observed on crumpled garments e.g., the wrinkles around the knees after sitting for a while. Such wrinkles can be easily recovered if not deformed for long, and otherwise be persistent. Since they are vital to the visual realism of cloth animation, we aim to simulate realistic looking persistent wrinkles. To this end, we present a physics-inspired finegrained wrinkle model. Different from existing methods, we recognize the importance of the interplay between internal friction and plasticity during wrinkle formation. Furthermore, we model their time dependence for persistent wrinkles. Our model is capable of not only simulating realistic wrinkle patterns, but also their time-dependent changes according to how long the deformation is maintained. Through extensive experiments, we show that our model is effective in simulating realistic spatial and temporal varying wrinkles, versatile in simulating different materials, and capable of generating more fine-grained wrinkles than the state of the art.
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.
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.
Constrained Spectral Uplifting for HDR Environment Maps
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2025) Tódová, L.; Wilkie, A.
Spectral representation of assets is an important precondition for achieving physical realism in rendering. However, defining assets by their spectral distribution is complicated and tedious. Therefore, it has become general practice to create RGB assets and convert them into their spectral counterparts prior to rendering. This process is called . While a multitude of techniques focusing on reflectance uplifting exist, the current state of the art of uplifting emission for image‐based lighting consists of simply scaling reflectance uplifts. Although this is usable insofar as the obtained overall scene appearance is not unrealistic, the generated emission spectra are only metamers of the original illumination. This, in turn, can cause deviations from the expected appearance even if the rest of the scene corresponds to real‐world data. In a recent publication, we proposed a method capable of uplifting HDR environment maps based on spectral measurements of light sources similar to those present in the maps. To identify the illuminants, we employ an extensive set of emission measurements, and we combine the results with an existing reflectance uplifting method. In addition, we address the problem of environment map capture for the purposes of a spectral rendering pipeline, for which we propose a novel solution. We further extend this work with a detailed evaluation of the method, both in terms of improved colour error and performance.
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.

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