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An AI rock-paper-scissors game is a three-stage pipeline: a camera supplies frames, a hand-tracking system finds hand landmarks, and a gesture classifier predicts rock, paper, scissors or an unknown state. Separate game logic then compares the recognized move with the computer’s move and decides the result. MediaPipe’s Gesture Recognizer supports still images, decoded video and live video, so the same design can power a photo demo or an interactive webcam game.
How the recognition pipeline works
Recognition and game rules should remain separate. The vision model answers “What hand pose is visible?” The game answers “Who wins this round?” Keeping those responsibilities apart makes it easier to replace the model, add players or change the rules without rewriting the entire application.
- Capture: Read a still image, video frame or live camera frame.
- Detect and localize: The hand-landmark stage identifies the hand and estimates its geometry. MediaPipe reports 21 hand-knuckle coordinates, including image coordinates and world coordinates.
- Classify: A gesture model maps the hand geometry to a category and score. The documented task can also return handedness and results for multiple hands.
- Validate: Apply a confidence threshold and accept only a stable, supported move. Otherwise return
noneor wait for a clearer pose. - Apply rules: Compare the accepted move with the opponent’s move and display the round result.
Google’s task documentation describes a model bundle containing hand-landmark and gesture-classification components. The landmark model was trained on approximately 30,000 real-world images plus rendered synthetic hand models across varied backgrounds. That figure describes landmark-model training, not rock-paper-scissors accuracy.
Choosing an input: image, video or webcam
| Input | Best use | What you need |
|---|---|---|
| Still image | Early experiments, debugging labels and demonstrations | An image supplied to the recognizer |
| Decoded video | Replay analysis or testing a recorded hand sequence | A video decoder and frame-processing loop |
| Live video | Interactive play against the computer | A camera stream and a loop that processes incoming frames |
A built-in camera is sufficient when it delivers usable frames. A separate USB webcam is optional, not a requirement; it is useful only when the computer lacks a suitable camera or its placement and image quality are inadequate.
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Using MediaPipe’s recognizer
The documented Gesture Recognizer provides a pretrained starting point and exposes settings for score thresholds and hand-presence confidence. It accepts the three input modes above and returns gesture categories, landmarks and handedness. For a game, treat the category as a prediction rather than certainty: reject low-score results, and require the same move to persist across several frames before locking a round.
Handling the none category
Google’s customization example uses four labels: rock, paper, scissors and none. The none label represents poses outside the named game gestures. Keeping that outcome prevents a partially visible, badly framed or unrelated hand pose from becoming an accidental move.
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Typical frame-state logic
- Process each frame and collect the top gesture and score.
- Discard frames with no detected hand or a score below your chosen threshold.
- Track consecutive predictions; reset the counter when the label changes or becomes
none. - When a label remains stable for your chosen number of frames, lock the player’s move.
- Stop accepting new frames until the round is resolved and the player shows
noneor otherwise resets the hand state.
The exact threshold and stability window depend on the camera, lighting, distance and users. They should be tuned with the hardware on which the game will run rather than copied as universal values.
Writing the rock-paper-scissors game logic
Once a valid label is available, the game no longer needs computer vision. Generate or select the opponent’s move, then apply the ordinary cycle:
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- Rock beats scissors.
- Scissors beats paper.
- Paper beats rock.
- Identical moves are a draw.
A clean implementation represents each move as a value and uses a small rule table or mapping to determine the outcome. Keep camera processing, move validation, opponent selection, scoring and display as separate functions or modules. This makes a “best of five” mode, a two-player mode or a non-camera test harness straightforward to add.
Pretrained model or custom gesture model?
| Approach | Advantages | Trade-offs |
|---|---|---|
| Pretrained Gesture Recognizer | Documented task, ready-made inference and standard outputs | Its built-in categories and behavior may not match your poses, camera angle or interaction design |
| Custom Model Maker recognizer | Labels and examples can be tailored to your users, framing and gestures | You must collect representative data, train, evaluate and export a model asset bundle |
| Landmark-plus-geometry rules | Simple, transparent logic; a public RPS example demonstrates angle-based classification after MediaPipe hand tracking | Rules can be brittle when viewpoint, occlusion or hand shape changes; no cited comparison establishes superior accuracy |
Custom training workflow
Google’s customization guide organizes images in folders by label and requires a none folder. The documented workflow runs the hand detector to obtain landmarks, trains a gesture classifier with Model Maker, evaluates it on a test split and exports a model asset bundle. The Google Developers Blog presents the same load, split, train, evaluate and export sequence as an example; it is a workflow description, not a promise that a small dataset will be reliable.
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Collect examples from the intended camera distance, backgrounds, lighting conditions, skin tones, hand orientations and users. Keep a held-out test set that is not used for training. Report the camera, lighting, number and identities of users, class balance and decision threshold whenever publishing a performance number.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Designing the webcam game loop
- Open the default camera or selected video device.
- Convert each captured frame into the format required by the recognizer.
- Run hand detection and gesture classification.
- Overlay the predicted label, score and optional landmarks for debugging.
- Pass only a validated, stable move to the game-state controller.
- Display the player’s move, the opponent’s move, the outcome and the running score.
- Release the camera and close the display window when the user exits.
A public NTU ARL example, 03_game_rps.py, illustrates this general arrangement with webcam capture, MediaPipe processing, an angle-based classifier and an OpenCV display loop. It demonstrates an implementation pattern, not a controlled benchmark or endorsement.
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Common failure modes and fixes
- No hand detected: Move the hand fully into frame, improve lighting and reduce distance from the camera.
- Moves change every frame: Add a confidence cutoff and temporal stability requirement instead of using one frame as the decision.
- Paper is confused with another pose: Add varied training examples or revise the geometry rules for the actual camera viewpoint.
- Accidental rounds: Require a
nonereset between rounds and ignore low-confidence predictions. - Two hands appear: Define whether the game accepts one designated hand or needs a policy for multiple detections; the task can report results for multiple hands.
- Performance varies by setup: Test the exact camera, lighting, framing and users planned for deployment. The cited sources establish no general accuracy percentage across those conditions.
What is and is not established about accuracy
Neither the cited task documentation nor the implementation examples establishes a general rock-paper-scissors accuracy, frame rate or latency figure across devices and environments. Repository claims such as “95% accuracy” should not be generalized without a named test set, hardware and evaluation method. A credible report should state those conditions and distinguish landmark-detection performance from the final game-label accuracy.
Source notes
The technical behavior described here follows Google AI Edge and Google for Developers documentation for the Gesture Recognizer and its hand-gesture customization guide, the Google Developers Blog’s Model Maker workflow example, and the NTU ARL 03_game_rps.py webcam implementation. IEEE Xplore’s 2025 “Gesture Showdown: Rock Paper Scissors with AI Vision” abstract describes a MediaPipe, OpenCV and camera-video approach, but it is evidence of a project method rather than independent performance validation.
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