Universal multi-person pose detection system for Unity supporting any pose model. Includes models. Perfect for fitness apps, games, AR/VR with real-time processing.Note: Package focuses on pose detection logic rather than rendering, ensuring broad compatibility across all render pipelines.Pose Detection Interface brings universal human pose detection to Unity projects using the industry-standard COCO pose keypoint format. Works with any COCO pose-segmentation model — v2 adds a full multi-person tracking pipeline, letterbox-correct preprocessing, OKS-based duplicate suppression, offline replay, Timeline integration, and a virtual gesture input device.Universal Model Support:- Compatible with ANY COCO pose-segmentation keypoint format model- Auto-detects End-to-End [1, N, 57] and Anchor-based [1, 56, N] output layouts- Pre-configured for all 17 COCO standard keypoints- Three declarative normalization modes (Unit01, Byte255, ImageNet)- Example ONNX models included- One-click model swap from the Inspector; hot-swap at runtime without stale callbacksAccuracy & Tracking (new in v2):- Letterbox preprocessing with gray padding — correct results on non-square cameras- Inverse coordinate mapping so keypoints always come back in source-frame pixels- Test-time horizontal-flip augmentation with anatomical left/right swap- Per-keypoint confidence thresholds- OKS (Object Keypoint Similarity) NMS using COCO sigmas- Multi-person tracking via Hungarian matching with augmented cost (torso distance + bbox-IoU + OKS + velocity)- Constant-velocity predictor so tracks survive short occlusions- Tentative → Confirmed state machine for flicker-free tracking IDs- Plug-in interface for custom appearance-based re-identificationKey Applications:- Fitness and workout tracking (squat counter included)- Interactive games with body movement input- Motion capture and pose-driven animation- AR/VR body tracking- Gesture-driven UI (virtual Input System device — bind gestures like any button)- Deterministic replays for QA, cutscenes, and Timeline-driven demosFully Customizable:- Complete source code included for custom implementations- Modular architecture — swap input sources, inference, parsing, smoothing, or visualizer independently- Extensive configuration exposed in the Inspector for performance tuning- Runtime (and Editor) assembly definitions for fast compile times- Profiler markers on every pipeline stage for targeted optimization- Cross-platform deployment including AndroidTooling (new in v2):- One-click editor menu scaffolds under GameObject → Pose Detection (auto-assigns a bundled model)- Offline PoseReplayer — play back recorded JSON without a camera or model- Timeline track + clip for deterministic pose playback- Gesture Input Driver — expose T-pose, hands-up, arms-crossed, wave, and more as Input System actions- Pose Live Comparison component for reference-clip matching- PoseRecorder with in-editor scrub / step / speed UIContent Includes:- Example ONNX models (PoseNet-v2 family: Nano, Small, Medium, Large, XLarge)- Seven example scenes: Real-time Detection, Video Input, Squat Counter, Pose Recorder, Pose Replayer, Gesture Detector, Stance Estimator- Ready-to-use prefabs for drag-and-drop integration- Comprehensive scripting API for custom model integrationMade by Yasin Shabani Varaki aka MrFresheyPAXTechnical DetailsKey Features:Universal COCO pose keypoint detection (17 standard keypoints)Support for ANY COCO pose-segmentation format modelReal-time processing with optimized performanceMulti-input support (webcam, video files, playlists)Android mobile platform optimizationPerformance monitoring and FPS trackingAutomatic error handling and recoveryDrag-and-drop prefab systemVisual pose keypoint renderingBatch video processing capabilitiesConfigurable confidence thresholdsUnity 6+ native compatibilityCross-platform deployment supportmodels includedCOCO Keypoint Standard: Nose, Eyes, Ears, Shoulders, Elbows, Wrists, Hips, Knees, AnklesSelected Category: AI & Machine LearningFeatures:Pose DetectionReal-time ProcessingMobile SupportVideo ProcessingNeural NetworksCOCO StandardSupported OS:WindowsmacOSLinuxAndroidAI assisted in drafting a clear, structured README covering features, setup, API usage, and troubleshooting. All AI-generated content was reviewed and edited to ensure accuracy and alignment with the package.The core pose detection algorithms, Unity integration, and creative decisions were developed independently. AI was used solely as a tool to support clarity and efficiency.
