Predicting Ray Pointer Landing Poses in VR Using Multimodal LSTM-Based Neural Networks
Bibliographic record
Abstract
Target selection is one of the most fundamental tasks in VR interaction systems. Prediction heuristics can provide users with a smoother interaction experience in this process. Our work aims to predict the ray landing pose for hand-based raycasting selection in Virtual Reality (VR) using a Long Short-Term Memory (LSTM)-based neural network with time-series data input of speed and distance over time from three different pose channels: hand, Head-Mounted Display (HMD), and eye. We first conducted a study to collect motion data from these three input channels and analyzed these movement behaviors. Additionally, we evaluated which combination of input modalities yields the optimal result. A second study validates raycasting across a continuous range of distances, angles, and target sizes. On average, our technique’s predictions were within 4.6° of the true landing Pose when 50% of the way through the movement. We compared our LSTM neural network model to a kinematic information model and further validated its generalizability in two ways: by training the model on one user’s data and testing on other users (cross-user) and by training on a group of users and testing on entirely new users (unseen users). Compared to the baseline and a previous kinematic method, our model increased prediction accuracy by a factor of 3.5 and 1.9, re spectively, when 40% of the way through the movement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".