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Rest Shape Optimization for Sag-Free Discrete Elastic Rods
No full textWe propose a new rest shape optimization framework to achieve sag-free simulations of discrete elastic rods. To optimize rest shape parameters, we formulate a minimization problem based on the kinetic energy with a regularizer while imposing box constraints on these parameters to ensure the system's stability. Our method solves the resulting constrained minimization problem via the Gauss-Newton algorithm augmented with penalty methods. We demonstrate that the optimized rest shape parameters enable discrete elastic rods to achieve static equilibrium for a wide range of strand geometries and material parameters.Computer Graphics ForumSoft Bodies, Strands, and Silks44
Fast Camera Calibration from Orthographic Views of Rotated Objects
No full textAccurate camera calibration is crucial for high-quality 3D reconstruction in computer vision applications. In industrial measuring scenarios, turntable sequences are often captured using telecentric lenses to overcome the foreshortening effect. While specialized Structure-from-Motion (SfM) solutions exist for orthographic projection, these methods are limited to textured objects. Approaches that leverage the scanned object's silhouette for camera calibration are independent of texture but are often restricted to smooth objects or require non-trivial optimization initializations to converge. In this work, we present a novel silhouette-based approach to estimate the rotation axis of a turntable under orthographic projection, extending the applicability to complex geometries, while requiring little to none parameter adjustments. By identifying the symmetry axis of the object's contour envelope and establishing frontier point correspondences on circular trajectories, we robustly estimate the azimuth and inclination angles of the rotation axis, enabling accurate camera pose computation. We evaluate our approach on synthetic datasets comprising four models with varying characteristics and compare it to a state-of-the-art orthographic SfM method, achieving comparable accuracy, while reducing computational cost 37-fold and eliminating reliance on object texture.Vision, Modeling, and VisualizationImaging and Image Processin
Visually Assessing 1-D Orderings of Contiguous Spatial Polygons
No full textOne-dimensional orderings of spatial entities have been researched in many contexts, e.g. spatial indexing structures or visualizations for spatiotemporal trend analysis. While plenty of studies have been conducted to evaluate orderings of point-based data, polygonal shapes, despite their different topological properties, have received less attention. Existing measures to quantify errors in projections or orderings suffer from generic neighborhood definitions and over-simplification of distances when applied to polygonal data. In this work, we address these shortcomings by introducing measures that adapt to a varying neighborhood size depending on the number of contiguous neighbors and thus, address the limitations of existing measures for polygonal shapes. To guide experts in determining a suitable ordering, we propose a user-steerable visual analytics prototype capable of locally and globally inspecting ordering errors, investigating the impact of geographic obstacles, and comparing ordering strategies using our measures.We demonstrate the effectiveness of our approach through a use case and conducted an expert study with 8 data scientists as a qualitative evaluation of our approach. Our results show that users are capable of identifying ordering errors, comparing ordering strategies on a global and local scale, as well as assessing the impact of semantically relevant geographic obstacles.Computer Graphics ForumSpatial and Multi-Scale Data Visualizatio
Optimal Dimensionality Selection Using Hull Heatmaps for Single-Cell Analysis
No full textSingle-cell RNA sequencing (scRNA-seq) has gained prominence as a valuable technique for examining cellular gene expression patterns at the individual cell level. In the analysis of scRNA-seq datasets, it is common practice to visualise a subset of principal components (PCs), obtained via principal component analysis (PCA), using dimensionality reduction techniques such as t-stochastic neighbour embedding (t-SNE). Determining the number of PCs (i.e. dimensionality) is a critical step that influences the outcome of single-cell analysis, and this process typically requires a labour-intensive manual assessment involving the inspection of numerous projection plots. To address this challenge, we present a visualisation system that assists analysts in efficiently determining the optimal dimensionality of scRNA-seq data. The proposed system employs two hull heatmaps, a cell type heatmap and a cluster heatmap, which offer comprehensive representations of target cells of multiple cell types across various dimensionalities through the utilisation of a convex hull-embedded colour map. The cell type heatmap shows overlaps between cell types, and the cluster heatmap compares cell clustering results. The proposed hull heatmaps effectively alleviate the labourious task of manually evaluating hundreds of projection plots for searching for the optimal dimensionality. Additionally, our system offers interactive visualisation of gene expression levels and an intuitive lasso selection tool, thereby enabling analysts to progressively refine the convex hulls on the hull heatmaps. We validated the usefulness of the proposed system through two quantitative evaluations and three case studies.Computer Graphics ForumOriginal Article44
Visual Analysis of Poker Hands for Individual Players
No full textPoker requires complex decisions and, to improve play, careful analysis. Typical analytics tools focus on individual hands, overlooking broader performance trends. This paper proposes an approach for both individual hand review and a more comprehensive gameplay analysis. The linked visualizations comprise a line chart for cumulative winnings, a scatterplot for winnings vs. hand rankings, a bar chart for detailed hand winnings, and an event sequence visualization of the selected hand.EuroVis 2025 - PostersDemo
Digital Memory as a Tool for Critical Knowledge in Restoration: a digital archive with Omeka S
No full textThis paper presents a digital archiving project for the Sanctuary of Madonna di Garufo (Central Italy), damaged in the 2016 earthquake. Developed within a doctoral research framework, the initiative aims to document and preserve a multi-type corpus produced during the restoration process. Implemented on the Omeka S platform, the model adopts international standards (Dublin Core, ISAD(G), RiC-CM) and aligns with best practices for long-term preservation (PREMIS). The project proposes a replicable model for managing cultural heritage documentation through interoperable technologies and community engagement, promoting cultural resilience in post-disaster contexts.Digital HeritagePoster
Developing MosArt: An Accessible System for High-Quality Technical Photography
No full textHigh-quality photography is essential in cultural heritage but is often associated with expensive equipment, which remains out of reach for many institutions. To address this gap, we present MosArt: a low-cost, modular system for high-resolution, automated photography of 2D and 2.5D objects. Combining open-source hardware and software, MosArt supports calibration, tiled acquisition, and focus stacking. This paper provides an overview of the system architecture and software components, and illustrates their application through selected case studies.Digital HeritagePoster
Mind-Mapping Data Analysis with LLMs: From Vision to First Steps
No full textWe explore how large language models (LLMs) can support real-time visual mapping of data analysis workflows. Building on an earlier vision, we investigate if and how LLMs can decompose analytic dialogues into ''analysis maps'' that capture key semantic units such as questions, datasets, tasks, and findings. Using two exemplar analyses, we test both post-hoc and interactive strategies for generating these maps and experiment with prompting techniques for structuring and updating them. Results, documented in Observable notebooks, suggest that LLMs can scaffold analysis-as-network meaningfully-laying the groundwork for user-facing systems and moving beyond purely textual forms of LLM-mediated analysis.Computer Graphics and Visual Computing (CGVC)Short Papers Session: Visualisatio
The Past Is All Around You: Augmenting Cultural Heritage On-Site
No full textDigitized cultural heritage (CH) artifacts frequently lose their original immersive and historical context when presented through traditional digital means. Situated visualization, particularly through augmented reality (AR), offers a promising avenue to reconnect artifacts with their authentic physical environments. In this work, our objective is to explore methods for designing effective AR-based visualizations to enhance user engagement and understanding in cultural heritage contexts. We share initial insights derived from literature reviews, prototyping, and preliminary evaluations focusing on prominent Austrian CH sites.EuroVis 2025 - PostersPosters and Demo
Trajectory-guided Anime Video Synthesis via Effective Motion Learning
No full textCartoon and anime motion production is traditionally labor-intensive, requiring detailed animatics and extensive inbetweening from keyframes. To streamline this process, we propose a novel framework that synthesizes motion directly from a single colored keyframe, guided by user-provided trajectories. Addressing the limitations of prior methods, which struggle with anime due to reliance on optical flow estimators and models trained on natural videos, we introduce an efficient motion representation specifically adapted for anime, leveraging CoTracker to capture sparse frame-to-frame tracking effectively. To achieve our objective, we design a two-stage learning mechanism: the first stage predicts sparse motion from input frames and trajectories, generating a motion preview sequence via explicit warping; the second stage refines these previews into high-quality anime frames by fine-tuning ToonCrafter, an anime-specific video diffusion model. We train our framework on a novel animation video dataset comprising more than 500,000 clips. Experimental results demonstrate significant improvements in animating still frames, achieving better alignment with user-provided trajectories and more natural motion patterns while preserving anime stylization and visual quality. Our method also supports versatile applications, including motion manga generation and 2D vector graphic animations. The data and code will be released upon acceptance. For models, datasets and additional visual comparisons and ablation studies, visit our project page: https://animemotiontraj.github.io/.Pacific Graphics Conference Papers, Posters, and DemosCharacter Animatio