vr game developers

Abstract: Nowadays, it is easy to set up any element of a VR experience so users can interact with it physically, except for interactive VR characters. Can we extend this to animated VR characters? In this work, we studied whether physics-based character animation (PBCA) controllers can produce high-quality movement in VR-compatible environments (Unity3D, WebXR). We evaluated the quality of the results, as well as the losses at each stage of the processing pipeline: data ingest, training and inference. For data ingest, we computed accuracy metrics related to the retargeting method used. For training, we computed accuracy metrics related to different training policies and different training environments. We developed new JAX implementations of state-of-the-art PBCA controllers (DeepMimic, AMP and ADD) which can be trained in MuJoCo-MJX in minutes, 50 times faster than in a traditional CPU-based training environment. The benefit of these particular implementations is that the resulting control policies can be readily transferred to Unity3D and support cross-platform compilation, including Android, something critical for use in VR production. For inference, we tested these controller policies in Unity3D and WebXR. Our results suggest that DeepMimic and ADD trained in MuJoCo-MJX generate controller policies with better animation fidelity than other methods. To simplify the comparison between controller policies, and different reference animations, we implemented an animation fidelity index (afi). Its results are analogous to other more common metrics, but afi is more intuitive to interpret and thus simplifies the comparison of training methods across the successive stages of the processing pipeline.

Abstract: We present three locomotion adaptation approaches: Motion Acceleration, Turn Acceleration, and Motion Deceleration to improve postural stability during body-state transitions in virtual reality (VR). The system detects standing-to-walking, turning, and walking-to-stopping transitions and applies adaptive locomotion smoothing. Motion Acceleration gradually increases locomotion speed when users begin walking, Turn Acceleration smooths rotation while turning, and Motion Deceleration gradually reduces movement speed before stopping. We evaluated these techniques in a virtual navigation task using objective and subjective balance measures. Preliminary results show reduced center of pressure (COP) velocity and improved balance confidence. These findings suggest that locomotion adaptations can improve balance and navigation experience.

Abstract: Hand tracking offers natural VR interaction but lacks controllers' inherent tactile feedback, while haptic feedback across input methods remains understudied. We conducted a participant study comparing vibration, impulse, and no feedback in hand- and controller-based VR interaction across grasp-, pinch-, and tap-like gestures assessing performance, workload, and preference. A custom glove and controller-mounted device provided both feedback types, aiming for consistency across input modalities. Controllers were faster for grasp and pinch, whereas hand tracking was more accurate for pinch and preferred overall. Haptics had limited performance effects, although impulse reduced grasp accuracy with controllers. Participants preferred vibration and impulse over no haptic feedback, favouring vibration overall. Our findings reveal a more nuanced relationship between haptic feedback, input device, and interaction context than suggested by previous work.

Abstract: Public VR game leaderboards contain tracked motion recordings uploaded by hundreds of thousands of users. Once uploaded, these recordings are accessible to anyone and create privacy risks (i.e., identification and profiling). Prior work has proposed mechanisms that modify tracked movement to reduce these risks. Their utility is commonly evaluated through physical deviation, where smaller deviations indicate better utility, while user studies are less common. However, it remains unclear how well physical deviation explains users' acceptance of a mechanism compared to user studies. We examine this through a user study of three VR motion privacy mechanisms at five physical deviation levels. We find that user studies explain substantially more variation in mechanism acceptance than physical deviation, although physical deviation remains significant. We also find that prior VR experience and exposure to VR privacy mechanisms significantly affect acceptance. We recommend combining physical deviation with user studies and reporting participants' prior experience.