English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Current Virtual Reality (VR) environments lack the rich haptic signals thathumans experience during real-life interactions, such as the sensation oftexture during lateral movement on a surface. Adding realistic haptic texturesto VR environments requires a model that generalizes to variations of a user'sinteraction and to the wide variety of existing textures in the world. Currentmethodologies for haptic texture rendering exist, but they usually develop onemodel per texture, resulting in low scalability. We present a deeplearning-based action-conditional model for haptic texture rendering andevaluate its perceptual performance in rendering realistic texture vibrationsthrough a multi part human user study. This model is unified over all materialsand uses data from a vision-based tactile sensor (GelSight) to render theappropriate surface conditioned on the user's action in real time. Forrendering texture, we use a high-bandwidth vibrotactile transducer attached toa 3D Systems Touch device. The result of our user study shows that ourlearning-based method creates high-frequency texture renderings with comparableor better quality than state-of-the-art methods without the need for learning aseparate model per texture. Furthermore, we show that the method is capable ofrendering previously unseen textures using a single GelSight image of theirsurface.

Development and Evaluation of a Learning-based Model for Real-time Haptic Texture Rendering