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The international journal Computer Graphics Forum (CGF) is jointly published by the Eurographics Association and Wiley (former Blackwell). CGF is a leading journal for in-depth technical articles on computer graphics.

  • DriveRNN: Predicting Drivers' Attention with Deep Recurrent Networks 

    Lasheras-Hernandez, Blanca; Masia, Belen; Martin, Daniel (The Eurographics Association, 2022)
    Lately, the automotive industry has experienced a significant development led by the ambitious objective of creating an autonomous vehicle. This entails understanding driving behaviors in different environments, which ...
  • Development of a Node-Based Material Editor 

    Leiro, Luciano Suaya; Garrigó, Marc (The Eurographics Association, 2022)
    Materials systems are an important element within the development of a renderer for an application such as a video game. Nowadays, the method to build a graphic style for a product involving a real-time engine implies a ...
  • NeodiagVR: Virtual Reality Apgar test environment 

    Ferry, Lucas; Gimeno, Jesús; Estañ, Francisco Javier; Núñez, Francisco; Balaguer, Evelin; Fernández, Marcos; Portalés, Cristina (The Eurographics Association, 2022)
    Owing to the lack of accessibility of postpartum rooms nowadays, which are needed to teach the correct assessment of newborn health status to medical students, virtual reality and simulation are increasingly used for ...
  • Interactive Visualization of Historical Fabrics at Yarn Level using Sphere Tracing 

    Pérez-Aixendri, Manolo; Gimeno, Jesús; Portalés, Cristina; Casanova-Salas, Pablo (The Eurographics Association, 2022)
    In this paper we present preliminary results of a new approach for interactive visualization of historical fabrics at yarn level based on models using Signed Distance Functions. They are compared with the results of Virtual ...
  • Kalathos+: Construcción de datasets para la clasificación automática de fragmentos de vasijas cerámicas de torno 

    Segura, Rafael J.; Rueda, Antonio J.; Ogáyar, Carlos J.; Fuertes, José M.; García-Fernández, Ángel L.; Lucena, Manuel J.; López, Alfonso; Moreno, Isabel; Molinos, Manuel (The Eurographics Association, 2022)
    En los últimos años las técnicas de aprendizaje profundo se han convertido en una poderosa alternativa a técnicas clásicas de aprendizaje automático orientadas a tareas de clasificación. Esta tecnología se ha aplicado con ...

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