Multimodal control of virtual game environments through gestures and physical controllers
Bibliographic record
Abstract
The control of virtual video game environments through body motion is recently of great interest to academic and industry research groups since it enables many new interactive experiences. With the recent growth in the availability of affordable 3D camera technology, researchers have increasingly investigated the control of games through body and hand gestures. In addition, the dropping cost of MEMS technology has increased the popularity of physical controllers incorporating accelerometers, gyroscopes, and other sensors. Existing work, however, has yet to combine the strengths of a 3D camera with those of a physical game controller to provide six degrees of freedom and one-to-one correspondence between the real-world 3D space and the virtual environment. In this paper, a human-computer interface is presented that allows users to manipulate 3D objects within a virtual space by simultaneously using one hand to perform gestures and the other hand to command a physical controller. This is accomplished by processing the data returned from a custom 3D depth camera to obtain hand gestures along with the absolute position of the controller-wielding hand. Through the use of a composite transformation matrix, this position data is fused with the orientation data measured from the instruments within the controller. The matrix is then applied to a 3D object within a virtual environment in realtime. Two prototype environments that combine hand gestures and a physical controller are used to evaluate this new method of interactive gaming.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".