EG 2019 - Posters

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/2632749

Browse

Browsing EG 2019 - Posters by Subject "Applied computing"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    The Effects of Adaptive Synchronization on Performance and Experience in Gameplay
    (The Eurographics Association, 2019) Watson, Benjamin; Gavane, Ajinkya; Shrivastava, Rachit; Fusiello, Andrea and Bimber, Oliver
    As graphics (GPU) hardware has improved, fixed refresh rate displays became a significant throttle on graphics performance. GPU and display manufacturers therefore introduced adaptive synchronization (Async), which allows displays to adaptively synchronize to GPUs, avoiding rendering stalls and improving frame rate mean and variation. This research is a first experimental examination of the effects of Async on the experience of dedicated (but not professional) gamers. Participants played a first-person shooter (FPS) game, both with Async on and with Async off. After each game session, we assessed participant emotional state and gaming performance. We learned that at least for this popular FPS, Async can improve gaming performance, and may also benefit experience. We also found that Async has intriguing relationships to game familiarity and years of gameplay that merit additional investigation. Further research should examine these relationships, as well as Async's effects in systems with higher frame rates.
  • Thumbnail Image
    Item
    VITON-GAN: Virtual Try-on Image Generator Trained with Adversarial Loss
    (The Eurographics Association, 2019) Honda, Shion; Fusiello, Andrea and Bimber, Oliver
    Generating a virtual try-on image from in-shop clothing images and a model person's snapshot is a challenging task because the human body and clothes have high flexibility in their shapes. In this paper, we develop a Virtual Try-on Generative Adversarial Network (VITON-GAN), that generates virtual try-on images using images of in-shop clothing and a model person. This method enhances the quality of the generated image when occlusion is present in a model person's image (e.g., arms crossed in front of the clothes) by adding an adversarial mechanism in the training pipeline.