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Item EUROGRAPHICS 2025: Posters Frontmatter(Eurographics Association, 2025) Günther, Tobias; Montazeri, Zahra; Günther, Tobias; Montazeri, ZahraItem Learning Proper Object Spacing with Polygon Rendering for Layout Rearrangement(The Eurographics Association, 2025) Sun, Kaifan; Xiao, Jun; Jiang, Haiyong; Günther, Tobias; Montazeri, ZahraSuccessful scene arrangement requires ensuring appropriate distances between objects and avoiding excessive overlaps or separations. This work proposes a method for automatically learning spatial relationships between objects in scene arrangement using a differentiable renderer loss. First, objects surrounding a dominant item (e.g., a table in a dining room) are identified and represented as nodes in a polygon that encodes their spatial relations. The difference between the predicted and ground truth polygons is minimized via a rendering loss, which is integrated into the training of a generative diffusion model. This approach continuously optimizes the spatial distribution of objects during generation, ensuring physical consistency and practical usability. Experimental results show a significant reduction in collision rates compared to state-of-the-art methods.Item Cage-based Deformation of Field Functions(The Eurographics Association, 2025) Grenier, Charline; Trancho, Kevin; Zanni, Cédric; Barthe, Loïc; Günther, Tobias; Montazeri, ZahraImplicit geometry is a popular representation for shape modelling. It provides several interesting properties, such as infinite resolution, continuity and smooth blending. However, implicit surfaces are difficult to deform as deformations need to be invertible. They are in general restricted to linear representations or more advanced translation-based deformations. We propose a method that adapts cage-based deformation to implicit surfaces while handling self-intersections in the deformed space.Item Perspective Crop Based Egocentric Hand Pose Estimation via Fisheye Stereo Vision(The Eurographics Association, 2025) Hur, Hyejin; Baek, Seongmin; Gil, Younhee; Kim, Sangpil; Günther, Tobias; Montazeri, ZahraIn this paper, we propose a method to improve the performance of hand pose estimation from egocentric view. To accurately capture hands moving within a wide range in daily activities, we mounted a fisheye stereo camera on a head mounted display to obtain wide-angle images from egocentric view. Our proposed two-stage method addresses the camera distortion introduced by this setup. The 2D hand keypoints estimated by stage-1 HandNet are converted into 3D hand keypoints through triangulation for perspective cropping. Stage-2 HandNet then predicts the final 2D hand keypoints from the undistorted hand crop image. To train stage-1 HandNet for perspective cropping, we built FisheyeEgoHAND dataset which consists of three categories of scenarios (separate hand, hand-hand, and hand-object) that reflect various hand interactions in an egocentric view. Through experiments, we demonstrated that two-stage 2D hand pose estimation outperforms one-stage approach without perspective cropping.Item Visual Agentic System for Spatial Metric Query Answering in Remote Sensing Images(The Eurographics Association, 2025) Wang, Yinghao; Wang, Cheng; Günther, Tobias; Montazeri, ZahraAccurately measuring real-world object dimensions from Remote Sensing (RS) images is crucial for applications in geospatial analysis and urban planning. Traditional Vision-Language Models (VLMs) struggle with spatial reasoning, while end-to-end remote sensing VLMs are often limited to predefined tasks such as image captioning. In this paper, we propose a visual agentic system for spatial metric query answering, dynamically integrating code-generation agents with a grounded remote sensing VLM and a Vision Specialist. Our system autonomously identifies reference objects, infers scale factors, and performs spatial measurements through structured subroutines. Experiments demonstrate that our approach achieves higher accuracy in footprint area estimation compared to state-of-the-art large language models with vision capabilities.Item Using Smartphone EXIF Data to Classify Lighting Conditions for Outdoor Augmented Reality(The Eurographics Association, 2025) Nikolov, Ivan; Mircov, Flavius-Alexandru; Villumsen, Jacob Holm; Larsen, Mike Lien; Madsen, Claus; Günther, Tobias; Montazeri, ZahraCorrectly matching real-world environment lighting conditions is an important step in making Augmented Reality content better fit with surrounding real objects. It is also the first step in larger, more complex problems like object relighting, shadow estimation, surface shading, etc. Dynamic classification of lighting conditions thus needs to be robust and lightweight. In this paper, we investigate the suitability of using pure EXIF data for classifying outdoor lighting conditions in four broad categories using a variety of shallow machine learning models. We gather a dataset of images together with EXIF metadata to test different models and show the results from the best-performing one in a real-time Augmented Reality application on a smartphone.Item A Gaze Prediction Model for Task-Oriented Virtual Reality(The Eurographics Association, 2025) Mammou, Konstantina; Mania, Katerina; Günther, Tobias; Montazeri, ZahraIn this work, we present a gaze prediction model for Virtual Reality task-oriented environments. Unlike past work which focuses on gaze prediction for specific tasks, we investigate the role and potential of temporal continuity in enabling accurate predictions in diverse task categories. The model reduces input complexity while maintaining high prediction accuracy. Evaluated on the OpenNEEDS dataset, it significantly outperforms baseline methods. The model demonstrates strong potential for integration into gaze-based VR interactions and foveated rendering pipelines. Future work will focus on runtime optimization and expanding evaluation across diverse VR scenarios.Item Sampling of Anisotropic Spatial Gaussians for Path Guiding(The Eurographics Association, 2025) Lelyakin, Sergey; Schüßler, Vincent; Dachsbacher, Carsten; Günther, Tobias; Montazeri, ZahraDirectional models in path guiding struggle with representing parallax effects or anisotropic features. Our model instead describes the spatial distribution of a target vertex using a 3D Gaussian mixture model. While this dispenses with the need for reprojection and allows to represent anisotropic features easily, its directional probability density is not readily available, since it involves a marginal integral. In this work, we derive an expression for the PDF of our model in solid angle measure that is practical to evaluate. We demonstrate how our model can improve guiding accuracy in various scenes.Item Interactive Sketch-based Modeling of Braided Hair(The Eurographics Association, 2025) Jetti, Hari Hara Gowtham; Parakkat, Amal Dev; Günther, Tobias; Montazeri, ZahraHair braids are widely used in various games and animated movies, thanks to their simplified representation and ease of animation. However, the existing research on modeling braids often relies on a limited dictionary of commonly seen hair braid patterns, constraining artists' ability to experiment by creating imaginary or creative hair braids. In this paper, we introduce a simple sketch-based interface for creating arbitrary hair braids. Our method employs a two-stage framework that first interprets a user-drawn sketch to extract the braid pattern. To accommodate arbitrarily drawn sketches, we then use a physics-inspired simulation to generate visually pleasing braids. In addition to automatically generating braids, our system allows users to interactively refine the braid pattern to create braids that match the user's imagination, facilitating experimentation and exploration of different braid structures.Item Automatic Image-Based Coral Polyp Analysis through Multi-View Instance Segmentation(The Eurographics Association, 2025) Dutta, Somnath; Pavoni, Gaia; Corsini, Massimiliano; Ganovelli, Fabio; Cignoni, Paolo; Rossi, Paolo; Cenni, Elena; Simonini, Roberto; Grassi, Francesca; Cassanelli, Davide; Cattini, Stefano; Rovati, Luigi; Capra, Alessandro; Castagnetti, Cristina; Günther, Tobias; Montazeri, ZahraWe present an automated framework for counting and measuring the polyps of Cladocora caespitosa, a Mediterranean reefbuilding coral. To our knowledge, the most practical method for counting polyps currently involves ecologists' visual inspection of a 3D model. However, measuring polyps from the model can lead to inaccuracies due to distortions in the reconstruction. Our method integrates deep learning-based instance segmentation on 2D images with 3D models for unique polyp identification, ensuring precise biometric extraction. The proposed pipeline automates polyp detection, counting, and measurement while overcoming the limitations of manual in situ methods. Laboratory validation demonstrates its accuracy and efficiency, paving the way for scalable, high-resolution phenotyping, and field monitoring of Mediterranean coral populations.