Hovering Over the Key to Text Input in XR
June 13, 2024 Β· Declared Dead Β· π 2024 IEEE International Symposium on Emerging Metaverse (ISEMV)
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Mar Gonzalez-Franco, Diar Abdlkarim, Arpit Bhatia, Stuart Macgregor, Jason Alexander Fotso-Puepi, Eric J Gonzalez, Hasti Seifi, Massimiliano Di Luca, Karan Ahuja
arXiv ID
2406.09579
Category
cs.HC: Human-Computer Interaction
Citations
2
Venue
2024 IEEE International Symposium on Emerging Metaverse (ISEMV)
Last Checked
4 months ago
Abstract
Virtual, Mixed, and Augmented Reality (XR) technologies hold immense potential for transforming productivity beyond PC. Therefore there is a critical need for improved text input solutions for XR. However, achieving efficient text input in these environments remains a significant challenge. This paper examines the current landscape of XR text input techniques, focusing on the importance of keyboards (both physical and virtual) as essential tools. We discuss the unique challenges and opportunities presented by XR, synthesizing key trends from existing solutions.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted