arXiv subjects

Suzanne Sorli

Publications and source records attributed to Suzanne Sorli.

3 recordsLinked to original sources

arXiv2026

Words have Weight: Comparing the use of pressure and weight as a metaphor in a User Interface in Virtual Reality

This work investigates how weight and pressure can function as haptic metaphors to support user interface notifications in Virtual Reality (VR). While prior research has explored ungrounded weight simulation and pneumatic feedback, their combined role in conveying information through UI elements remains underexplored. We developed a wearable haptic device that transfers liquid and air into flexible containers mounted on the back of the user's hand, allowing us to independently manipulate weight and pressure. Through an initial evaluation using three conditions-no feedback, weight only, and weight combined with pressure-we examined how these signals affect perceived heaviness, coherence with visual cues, and the perceived urgency of notifications. Our results validate that pressure amplifies the perception of weight, but this increased heaviness does not translate into higher perceived urgency. These findings suggest that while pressure___enhanced weight can enrich haptic rendering of UI elements in VR, its contribution to communicating urgency may require further investigation, alternative pressure profiles, or different types of notifications.

cs.GR↗

arXiv2025

Say it or AI it: Evaluating Hands-Free Text Correction in Virtual Reality

Text entry in Virtual Reality (VR) is challenging, even when accounting for the use of controllers. Prior work has tackled this challenge head-on, improving the efficiency of input methods. These techniques have the advantage of allowing for relatively straightforward text correction. However, text correction without the use of controllers is a topic that has not received the same amount of attention, even though it can be desirable in several scenarios, and can even be the source of frustration. Large language models have been adopted and evaluated as a corrective methodology, given their high power for predictions. Nevertheless, their predictions are not always correct, which can lead to lower usability. In this paper, we investigate whether, for text correction in VR that is hands-free, the use of AI could surpass in terms of usability and efficiency. We observed better usability for AI text correction when compared to voice input.

cs.HC↗

arXiv2021

RGB2Hands: Real-Time Tracking of 3D Hand Interactions from Monocular RGB Video

Tracking and reconstructing the 3D pose and geometry of two hands in interaction is a challenging problem that has a high relevance for several human-computer interaction applications, including AR/VR, robotics, or sign language recognition. Existing works are either limited to simpler tracking settings (e.g., considering only a single hand or two spatially separated hands), or rely on less ubiquitous sensors, such as depth cameras. In contrast, in this work we present the first real-time method for motion capture of skeletal pose and 3D surface geometry of hands from a single RGB camera that explicitly considers close interactions. In order to address the inherent depth ambiguities in RGB data, we propose a novel multi-task CNN that regresses multiple complementary pieces of information, including segmentation, dense matchings to a 3D hand model, and 2D keypoint positions, together with newly proposed intra-hand relative depth and inter-hand distance maps. These predictions are subsequently used in a generative model fitting framework in order to estimate pose and shape parameters of a 3D hand model for both hands. We experimentally verify the individual components of our RGB two-hand tracking and 3D reconstruction pipeline through an extensive ablation study. Moreover, we demonstrate that our approach offers previously unseen two-hand tracking performance from RGB, and quantitatively and qualitatively outperforms existing RGB-based methods that were not explicitly designed for two-hand interactions. Moreover, our method even performs on-par with depth-based real-time methods.

cs.CV↗