Eurographics Digital Library
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Recent Submissions
Deep High Dynamic Range Imaging: Reconstruction, Generation and Display
(2025-07-04) Chao Wang
High Dynamic Range (HDR) images offer significant advantages over Low Dynamic Range (LDR) images, including greater bit depth, a wider color gamut, and a higher dynamic range. These features not only provide users with an enhanced visual experience but also facilitate post-production processes in photography and filmmaking. Despite the considerable advancements in HDR technology over the years, significant challenges persist in the acquisition and display of HDR content. This thesis systematically explores the potential of leveraging deep learning techniques combined with physical prior knowledge to address these challenges. First, it investigates how implicit neural representations can be utilized to reconstruct all-in-focus HDR images from sparse, defocused LDR inputs, enabling flexible refocusing and re-exposure. Additionally, it extends the scope to the 3D domain by employing 3D Gaussian Splatting to reconstruct HDR all-in-focus fields from multi-view LDR defocused images, supporting novel view synthesis with refocusing and re-exposure capabilities. Expanding further, the thesis investigates strategies for generating HDR content from the in-the-wild LDR data or limited HDR datasets, and subsequently utilizes the resulting HDR generative models as priors to enable the transformation of LDR images into HDR. Finally, it proposes a feature contrast masking loss inspired by visual masking theory, enabling a self-supervised learning tone mapper to display the HDR content on LDR devices.
Efficient Computational Models for Forward and Inverse Elasticity Problems
(ETH Zurich, 2025-05-28) Li, Yue
Elasticity is at the core of many scientific and engineering applications, including the design of resilient structures and advanced materials, and the modeling of biological tissues. Simulating elastic systems poses significant computational challenges due to the inherent nonlinearity of the governing equations, which calls for efficient optimization methods to determine equilibrium states. Second-order methods are particularly attractive because of their superior convergence properties relative to first-order techniques. However, the effective use of second-order solvers requires that the underlying functions and their derivatives are sufficiently smooth and available in closed form. This smoothness can easily degrade when generalizing standard computational models to a broader set of design tasks. This thesis proposes efficient computational models that enable robust and effective simulations for physics-based modeling and the design of complex elastic systems. In chapter~\ref{chapter:PDW}, we propose a novel fabric-like metamaterial that features persisting contacts between 3D-printed yarns. To avoid the complexities of explicit contact modeling, we adopt an Eulerian-on-Lagrangian simulation paradigm; however, current methods remain limited to straight rods. We leverage a $C^2$-continuous representation to allow for Newton-type minimization on naturally curved rods. Chapter~\ref{chapter:DiffGD} presents a computational paradigm for intrinsic minimization of distance-based objectives defined on triangle meshes. Although Euclidean distances meet the $C^2$-continuity requirement, geodesic distances on triangle meshes do not. To permit efficient second-order optimization of embedded elasticity problems, we provide analytical derivatives as well as suitable mollifiers to recover $C^2$-continuity. Finally, in chapter~\ref{chapter:NMN}, we address non-smoothness issues that arise in nonlinear material design, where changes in geometry parameters can lead to discontinuous changes in simulation meshes. We employ neural networks with tailored nonlinearities as $C^\infty$-continuous and differentiable representations to characterize the elastic properties of families of mechanical metamaterials. The resulting smooth representation enables gradient-based inverse design for various high-level design goals.
Avatar Animations and Audio Fillers for Managing Response Delays
(The Eurographics Association, 2025) Singaravelan, Gopi Krishnan; Lay, Zhi Lynn; Han, Ping-Hsuan; Christie, Marc; Han, Ping-Hsuan; Lin, Shih-Syun; Pietroni, Nico; Schneider, Teseo; Tsai, Hsin-Ruey; Wang, Yu-Shuen; Zhang, Eugene
This study presents techniques for managing response delays in avatars with large language models (LLMs) to enhance user interaction. While existing avatar-based LLMs focus on human-like conversational abilities, they often overlook the impact of response delays on user experience. Our system strategically reframes these delays as opportunities to enhance the perceived humanness of the avatar by incorporating emotion-based animations, a companion pet, and contextually appropriate audio fillers. Through thoughtful audio-visual design and user interface enhancements during waiting periods, the demo showcases how effective delay management can sustain engagement, foster natural interactions, and turn waiting moments into meaningful elements of the conversational experience.
Exploring Perceptual Homogenization through a VR-Based AI Narrative
(The Eurographics Association, 2025) Kao, Bing-Chen; Tsai, Tsun-Hung; Christie, Marc; Han, Ping-Hsuan; Lin, Shih-Syun; Pietroni, Nico; Schneider, Teseo; Tsai, Hsin-Ruey; Wang, Yu-Shuen; Zhang, Eugene
This research explores how the drive for cognitive efficiency in Artificial Intelligence (AI) may contribute to the homogenization of sensory experiences. We present Abstract.exe, a Virtual Reality (VR) installation designed as a critical medium for this inquiry. The experience places participants in a detailed virtual forest where their exploration triggers an AI-driven ''simplification'' of the world. Visuals, models, and lighting progressively degrade, aiming to transform the 3D scene into abstract 2D color fields. This work attempts to translate the abstract logic of AI-driven summarization into a tangible, immersive experience. This paper outlines the concept and technical implementation in Unreal Engine 5 (UE5), which utilizes a Procedural Content Generation (PCG) framework. Abstract.exe is intended as both an artistic inquiry and a cautionary exploration of how we might preserve experiential richness in an algorithmically influenced world.
ServeSense: Interactive VR Tennis Serve Training System Enhanced with Haptic Feedback
(The Eurographics Association, 2025) Tsao, Chi; Shan, Ryan; Yu, Neng-Hao; Pan, Tse-Yu; Christie, Marc; Han, Ping-Hsuan; Lin, Shih-Syun; Pietroni, Nico; Schneider, Teseo; Tsai, Hsin-Ruey; Wang, Yu-Shuen; Zhang, Eugene
Tennis serves are a crucial aspect of the sport, often dictating the rhythm of a match and providing players with a competitive edge. While previous studies have explored the use of virtual reality (VR) to enhance tennis performance, they have largely overlooked the significance of tennis serves. To address this gap, the proposed ServeSense integrates gaming elements with Arduino hardware to enhance realism through haptic feedback. Users train in a gamified, fantasy environment, engaging in immersive and interactive tennis serve challenges. The system is designed to motivate user engagement while improving training efficiency.