Physically Plausible Realistic Grip-Lift Interaction Based on Hand Kinematics in VR
Bibliographic record
Abstract
Immersive technology, refers to various novel ways of creating and interacting with applications and experiences, e.g., virtual reality (VR), has been used in various simulations and training where preparing real/physical settings is not ideal or possible, or where the use of virtual contents is otherwise beneficial. Realizing realistic interactions with virtual content is crucial for a quality experience and the effectiveness of such simulation and training. In this paper, we propose a kinematics-based realistic hand interaction method to enable a physically plausible grip-lifting experience in VR. The method reflects three kinematic characteristics of the hand: the force at contact points, finger flexion, and the speed of hand/finger motion, and we developed a grip-lift interaction prototype using the proposed method. To examine the sense of realism and hand poses during the grip-lift interaction, we conducted a human subjects experiment using the prototype, resulting in positive effects on the perceived realism and usefulness of the interaction. Grip-lifting is a fundamental interaction technique that is involved in most embodied interaction scenarios. Our method would contribute to the design and development of realistic virtual experiences, of which we will discuss the implications and potential based on our findings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".