3D sketching in virtual reality (VR) enables users to create 3D virtual objects intuitively and immersively. However, previous studies showed that mid-air drawing may lead to inaccurate sketches. To address this issue, we propose to use one hand as a canvas proxy and the index finger of the other hand as a 3D pen. To this end, we first perform a formative study to compare two-handed interaction with tablet-pen interaction for VR sketching. Based on the findings of this study, we design HandPainter, a VR sketching system which focuses on the direct use of two hands for 3D sketching without requesting any tablet, pen, or VR controller. Our implementation is based on a pair of VR gloves, which provide hand tracking and gesture capture. We devise a set of intuitive gestures to control various functionalities required during 3D sketching, such as canvas panning and drawing positioning. We show the effectiveness of HandPainter by presenting a number of sketching results and discussing the outcomes of a user study-based comparison with mid-air drawing and tablet-based sketching tools.

HandPainter - 3D Sketching in VR with Hand-based Physical Proxy

Vibrotactile feedback in virtual motor learning: A systematic review.

When users create hand-drawn annotations in Virtual Reality they often reach their physical limits in terms of precision, especially if the region to be annotated is small. One intuitive solution employs magnification beyond natural scale. However, scaling the whole environment results in wrong assumptions about the coherence between physical and virtual space. In this paper, we introduce Mag-noramas, a novel interaction method for selecting and extracting a region of interest that the user can subsequently scale and transform inside the virtual space. Our technique enhances the user's capabilities to perform supernaturally precise virtual annotations on virtual objects. We explored our technique in a user study within asimplified clinical scenario of a teleconsultation-supported craniectomy procedure that requires accurate annotations on a human head. Teleconsultation was performed asymmetrically between a remote expert in Virtual Reality that collaborated with a local user through Augmented Reality. The remote expert operates inside a reconstructed environment, captured from RGB-D sensors at the local site, and is embodied by an avatar to establish co-presence. The results show that Magnoramas significantly improve the precision of annotations while preserving usability and perceived presence measures compared to the baseline method. By hiding the 3D reconstruction while keeping the Magnorama, users can intentionally choose to lower their perceived social presence and focus on their tasks.

Magnoramas: Magnifying Dioramas for Precise Annotations in Asymmetric 3D Teleconsultation

We contribute a novel user- and activity-independent kinematics-based regressive model for continuously predicting ballistic hand movements in virtual reality (VR). Compared to prior work on end-point prediction, continuous hand trajectory prediction in VR enables an early estimation of future events such as collisions between the user’s hand and virtual objects such as UI widgets. We developed and validated our prediction model through a user study with 20 participants. The study collected hand motion data with a 3D pointing task and a gaming task with three popular VR games. Results show that our model can achieve a low Root Mean Square Error (RMSE) of 0.80 cm, 0.85 cm and 3.15 cm from future hand positions ahead of 100 ms, 200 ms and 300 ms respectively across all the users and activities. In pointing tasks, our predictive model achieves an average angular error of 4.0° and 1.5° from the true landing position when 50% and 70% of the way through the movement. A follow-up study showed that the model can be applied to new users and new activities without further training.

https://dl.acm.org/doi/pdf/10.1145/3472749.3474753

So Predictable! Continuous 3D Hand Trajectory Prediction in Virtual Reality

Digital pen interaction has become a first-class input modality for precision tasks such as writing, annotating, drawing, and 2D manipulation. The key enablers of digital inking are the capacitive or resistive sensors that are integrated in contemporary tablet devices. In Virtual Reality (VR), however, users typically provide input across large regions, hence limiting the suitability of using additional tablet devices for accurate pen input. In this paper, we present Flashpen, a digital pen for VR whose sensing principle affords accurately digitizing hand writing and intricate drawing, including small and quick turns. Flashpen re-purposes an inexpensive gaming mouse sensor that digitizes extremely fine grained motions in the micrometer range at over 8 kHz when moving on a surface. We combine Flashpen's high-fidelity relative input with the absolute tracking cues from a VR headset to enable pen interaction across a variety of VR applications. In our two-block evaluation, which consists of a tracing task and a writing task, we compare Flashpen to a professional drawing tablet (Wacom). With this, we demonstrate that Flashpen's fidelity matches the performance of state-of-the-art digitizers and approaches the fidelity of analog pens, while adding the flexibility of supporting a wide range of flat surfaces.

Flashpen: A High-Fidelity and High-Precision Multi-Surface Pen for Virtual Reality

In spite of the great potential of on-body vibrotactile displays for a variety of applications, research lacks an understanding of the spacing between vibrotactile actuators. Through two experiments, we systematically investigate vibrotactile perception on the wrist, forearm, upper arm, back, torso, thigh, and leg, each in transverse and longitudinal body orientation. In the first experiment, we address the maximum distance between vibration motors that still preserves the ability to generate phantom sensations. In the second experiment, we investigate the perceptual accuracy of localizing vibrations in order to establish the minimum distance between vibration motors. Based on the results, we derive VibroMap, a spatial map of the functional range of inter-motor distances across the body. VibroMap supports hardware and interaction designers with design guidelines for constructing body-worn vibrotactile displays.

VibroMap: Understanding the Spacing of Vibrotactile Actuators across the Body

Human body postures are an important input modality for motion guidance and other application domains in HCI, e.g. games, character animations, and interaction with public displays. However, for training and guidance of body postures prior research had to define their own whole body gesture sets. Hence, the interaction designs and evaluation results are difficult to compare, due to a lack of a standardized posture set. In this work, we contribute APS (APS Posture Set), a novel posture set including 40 body postures. It is based on prior research, sports, and body language. For each identified posture, we collected 3D posture data using a Microsoft Kinect. We make the skeleton data, 3D mesh objects and SMPL data available for future research. Taken together, APS can be used to facilitate design of interfaces that use body gestures and as a reference set for future user studies and system evaluations.

APS: a 3D human body posture set as a baseline for posture guidance

Accurate sketching in virtual 3D environments is challenging due to aspects like limited depth perception or the absence of physical support. To address this issue, we propose VRSketchPen – a pen that uses two haptic modalities to support virtual sketching without constraining user actions: (1) pneumatic force feedback to simulate the contact pressure of the pen against virtual surfaces and (2) vibrotactile feedback to mimic textures while moving the pen over virtual surfaces. To evaluate VRSketchPen, we conducted a lab experiment with 20 participants to compare (1) pneumatic, (2) vibrotactile and (3) a combination of both with (4) snapping and no assistance for flat and curved surfaces in a 3D virtual environment. Our findings show that usage of pneumatic, vibrotactile and their combination significantly improves 2D shape accuracy and leads to diminished depth errors for flat and curved surfaces. Qualitative results indicate that users find the addition of unconstraining haptic feedback to significantly improve convenience, confidence and user experience.

VRSketchPen: Unconstrained Haptic Assistance for Sketching in Virtual 3D Environments

Computer-supported posture guidance is used in sports, dance training, expression of art with movements, and learning gestures for interaction. At present, the influence of display types and visualizations have not been investigated in the literature. These factors are important as they directly impact perception and cognitive load, and hence influence the performance of participants. In this paper, we conducted a controlled experiment with 20 participants to compare the use of five display types with different screen sizes: smartphones, tablets, desktop monitors, TVs, and large displays. On each device, we compared three common visualizations for posture guidance: skeletons, silhouettes, and 3d body models. To conduct our assessment, we developed a mobile and cross-platform system that only requires a single camera. Our results show that compared to a smartphone display, larger displays show a lower error (12%). Regarding the choice of visualization, participants rated 3D body models as significantly more usable in comparison to a skeleton visualization.

https://dl.acm.org/doi/pdf/10.1145/3461778.3462026

CameraReady: Assessing the Influence of Display Types and Visualizations on Posture Guidance

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VibroMap: Understanding the Spacing of Vibrotactile Actuators across the Body

APS: a 3D human body posture set as a baseline for posture guidance

VRSketchPen: Unconstrained Haptic Assistance for Sketching in Virtual 3D Environments

CameraReady: Assessing the Influence of Display Types and Visualizations on Posture Guidance