AIMC Topic: Computer Graphics

Clear Filters Showing 1 to 10 of 215 articles

BHGNN-RT: Capturing bidirectionality and network heterogeneity in graphs.

PloS one
Graph neural networks (GNNs) have shown great promise for representation learning on complex graph-structured data, but existing models often fall short when applied to directed heterogeneous graphs. In this study, we proposed a novel embedding metho...

TempODEGraphNet: predicting user churn using dynamic social graphs and neural ODEs.

PloS one
Research on user churn prediction has been conducted across various domains for a long time. Among these, the gaming domain is characterized by its potential for diverse types of interactions between users. Due to this characteristic, many studies on...

SensARy Substitution: Augmented Reality Techniques to Enhance Force Perception in Touchless Robot Control.

IEEE transactions on visualization and computer graphics
The lack of haptic feedback in touchless human-robot interaction is critical in applications such as robotic ultrasound, where force perception is crucial to ensure image quality. Augmented reality (AR) is a promising tool to address this limitation ...

FovealNet: Advancing AI-Driven Gaze Tracking Solutions for Efficient Foveated Rendering in Virtual Reality.

IEEE transactions on visualization and computer graphics
Leveraging real-time eye tracking, foveated rendering optimizes hardware efficiency and enhances visual quality virtual reality (VR). This approach leverages eye-tracking techniques to determine where the user is looking, allowing the system to rende...

Hit Around: Substitutional Moving Robot for Immersive and Exertion Interaction with Encountered-Type Haptic.

IEEE transactions on visualization and computer graphics
Previous works have shown the potential of immersive technologies to make physical activities a more engaging experience. With encountered-type haptic feedback, users can perceive a more realistic sensation for exertion interaction in substitutions r...

Enhancing Patient Acceptance of Robotic Ultrasound through Conversational Virtual Agent and Immersive Visualizations.

IEEE transactions on visualization and computer graphics
Robotic ultrasound systems have the potential to improve medical diagnostics, but patient acceptance remains a key challenge. To address this, we propose a novel system that combines an AI-based virtual agent, powered by a large language model (LLM),...

A graph neural network explainability strategy driven by key subgraph connectivity.

Journal of biomedical informatics
Current explainability strategies for Graph Neural Networks (GNNs) often focus on individual nodes or edges, neglecting the significance of key subgraphs in decision-making processes. This limitation can result in dispersed and less reliable explanat...

Large Language Model-Driven 3D Hyper-Realistic Interactive Intelligent Digital Human System.

Sensors (Basel, Switzerland)
Digital technologies are undergoing comprehensive integration across diverse domains and processes of the human economy, politics, culture, society, and ecological civilization. This integration brings forth novel concepts, formats, and models. In th...

Disentangled Active Learning on Graphs.

Neural networks : the official journal of the International Neural Network Society
Active learning on graphs (ALG) has emerged as a compelling research field due to its capacity to address the challenge of label scarcity. Existing ALG methods incorporate diversity into their query strategies to maximize the gains from node sampling...

Learning Pose Controllable Human Reconstruction With Dynamic Implicit Fields From a Single Image.

IEEE transactions on visualization and computer graphics
Recovering a user-special and controllable human model from a single RGB image is a nontrivial challenge. Existing methods usually generate static results with an image consistent subject's pose. Our work aspires to achieve pose-controllable human re...