Multimodal control of virtual game environments through gestures and physical controllers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it