Gesture Recognition Using MediaPipe for Online Realtime Gameplay
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
Hand gesture recognition has advanced greatly in the recent years due to its effectiveness in interacting with computers, machines, and other equipment and devices. It has been used in various fields such as hospitals, sign language recognition. While applications of the techniques for traditional video games at this current stage are still quite limited, we see the potentials using gesture-based controls as training and rehabilitation tools. This paper explores in particular how gestures can be used to play video games. It uses the gestures captured via the user’s video camera and performs various actions in the game. Two games, Hill Climb Racing and Subway Surfers, have been investigated in this paper. For Hill Climb Racing, only two hand gestures are needed since there are only two actions in the game. Body gestures and movements are used to play the game subway surfers. Promising results (real time with webcam only) indicating practicability of the approach are obtained.
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 | 0.001 | 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