{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_posetracker-api","slug":"posetracker-api","name":"PoseTracker API","type":"api","url":"https://posetracker.com","page_url":"https://unfragile.ai/posetracker-api","categories":["llm-apis"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_posetracker-api__cap_0","uri":"capability://image.visual.real.time.single.person.skeletal.pose.estimation.from.video.stream","name":"real-time single-person skeletal pose estimation from video stream","description":"Processes continuous video input (webcam, file, or streaming source) to detect and track a single human skeleton in real-time, outputting joint coordinates and confidence scores for 17-25 keypoints (depending on model variant). Uses deep neural network inference (likely convolutional backbone with heatmap regression or keypoint detection heads) optimized for low-latency inference on consumer hardware. Operates on standard RGB frames without requiring depth sensors, IR markers, or specialized capture equipment.","intents":["I need to capture body motion from a webcam for a fitness app without buying motion capture hardware","I want to track pose in real-time for a live streaming overlay or interactive game mechanic","I need skeletal joint data to drive character animation in my indie game engine","I'm building a form-correction tool that analyzes user posture during exercise"],"best_for":["indie game developers building character-driven mechanics","fitness and wellness app creators","solo developers prototyping motion-based interactions","content creators adding pose-driven visual effects to streams"],"limitations":["Single-person tracking only — no multi-person pose detection for group scenarios or sports analytics","Accuracy degrades significantly under poor lighting, extreme occlusion (limbs out of frame), or non-standard body types; no published variance metrics across conditions","Latency and accuracy characteristics at sustained high frame rates (60+ fps) not documented","No built-in temporal smoothing or filtering — raw keypoint jitter may require client-side post-processing","Requires clear frontal or near-frontal view; side/back angles may produce unreliable joint estimates"],"requires":["Webcam or video file input (H.264, VP9, or raw RGB frames)","Network connectivity for API calls (unless edge deployment option exists)","Valid API key from PoseTracker freemium or paid tier","Client-side video capture capability (browser WebRTC, desktop app, or mobile SDK)"],"input_types":["video stream (RTMP, HLS, WebRTC, or raw frames)","image file (JPEG, PNG)","live webcam feed"],"output_types":["JSON with keypoint coordinates (x, y, confidence per joint)","skeletal hierarchy representation","frame-by-frame pose data with timestamps"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_posetracker-api__cap_1","uri":"capability://data.processing.analysis.pose.keypoint.confidence.scoring.and.filtering","name":"pose keypoint confidence scoring and filtering","description":"Returns per-joint confidence scores (typically 0.0–1.0) indicating model certainty for each detected keypoint, enabling developers to filter or weight unreliable detections. Confidence reflects the neural network's activation strength at that joint location and implicitly encodes uncertainty from occlusion, motion blur, or ambiguous body configuration. Developers can threshold confidence to discard low-quality keypoints before downstream processing (animation, physics, analytics).","intents":["I need to ignore unreliable joint detections to prevent jittery or erratic character animation","I want to detect when a user's form is unclear and prompt them to reposition for better tracking","I need to weight pose data by confidence when blending multiple pose sources or smoothing over time","I'm building a quality gate that only logs pose data above a confidence threshold for training analytics"],"best_for":["developers building robust pose-driven UX that degrades gracefully under poor conditions","fitness app creators validating user form quality before logging workout data","game developers filtering noisy pose input to prevent animation glitches"],"limitations":["Confidence scores are model-relative and not calibrated to absolute accuracy — a 0.8 confidence joint may still be 5–10cm off in 3D space depending on camera angle and lighting","No published correlation between confidence score and actual positional error variance","Confidence does not account for temporal consistency — a joint can have high confidence in frame N and frame N+1 but represent physically impossible positions","Threshold tuning is empirical and application-specific; no guidance on recommended confidence cutoffs"],"requires":["API response parsing capability in client code","Understanding of confidence score semantics (0.0 = no detection, 1.0 = high certainty)"],"input_types":["pose estimation output (JSON with per-keypoint confidence)"],"output_types":["filtered keypoint list (subset of original joints above threshold)","confidence-weighted pose data","quality metrics (% of joints above threshold)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_posetracker-api__cap_2","uri":"capability://data.processing.analysis.frame.by.frame.pose.tracking.with.temporal.keypoint.output","name":"frame-by-frame pose tracking with temporal keypoint output","description":"Processes video frame-by-frame and outputs pose data for each frame with timestamps, enabling temporal analysis and motion reconstruction. Each frame produces a complete skeleton snapshot (all joint positions and confidences at that moment), allowing developers to compute velocity, acceleration, and motion patterns over time. Output is typically JSON arrays indexed by frame number or timestamp, preserving frame-to-frame correspondence for animation playback or motion analysis.","intents":["I need to extract motion data frame-by-frame to drive character animation in my game engine","I want to analyze movement velocity and acceleration to detect fast vs slow motions","I need to reconstruct a motion sequence for playback or export to animation software","I'm building a motion analysis tool that computes metrics like stride length or joint rotation over time"],"best_for":["game developers building animation systems driven by live pose input","motion analysis researchers and sports scientists","fitness app creators computing workout metrics (rep count, movement speed)"],"limitations":["No built-in temporal smoothing or interpolation — raw frame data may exhibit jitter between consecutive frames","Frame rate dependency: output quality tied to input video frame rate; low-fps input (15 fps) produces coarse temporal resolution","Timestamp accuracy depends on video source synchronization; streaming sources may have variable latency","No automatic gap-filling for occlusion — if a joint disappears in frame N, no interpolation to frame N+1 is provided","Memory overhead for storing full skeleton per frame; large videos (hours of footage) require streaming or chunked processing"],"requires":["Video input with consistent frame rate metadata","Client-side temporal buffering or streaming capability","Timestamp synchronization between video source and pose API"],"input_types":["video file with frame rate metadata","live video stream with frame timestamps"],"output_types":["JSON array of pose frames with timestamps","frame-indexed keypoint coordinates and confidences","motion vectors (velocity, acceleration per joint)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_posetracker-api__cap_3","uri":"capability://tool.use.integration.rest.api.endpoint.for.pose.inference.with.configurable.model.variants","name":"rest api endpoint for pose inference with configurable model variants","description":"Exposes HTTP endpoints accepting video frames or file uploads, returning pose data in JSON format. Likely supports multiple model variants (e.g., lightweight for mobile, high-accuracy for desktop) selectable via query parameters or request headers. Inference runs server-side, abstracting model loading and GPU management from the client. Responses include pose keypoints, confidences, and metadata (model version, inference time, frame dimensions).","intents":["I want to send video frames to a cloud API and get pose data back without managing ML infrastructure","I need to choose between fast (low-latency) and accurate (high-precision) pose models depending on my use case","I'm building a web app and need a simple HTTP interface to pose estimation without installing local dependencies","I want to scale pose inference across multiple concurrent requests without provisioning my own GPU servers"],"best_for":["web developers building browser-based pose apps","teams without ML infrastructure expertise","startups needing rapid prototyping without DevOps overhead","mobile app developers avoiding on-device model deployment"],"limitations":["Network latency adds 50–500ms per request depending on geographic distance and payload size; unsuitable for sub-100ms latency requirements","API rate limits and quota enforcement not documented; unclear if freemium tier supports sustained high-throughput use","Server-side inference costs scale with request volume; pricing model for production use not clearly published","No apparent support for batch processing (multiple frames in single request) — each frame requires separate API call, multiplying latency","Dependency on external service availability; no SLA or uptime guarantees documented for freemium tier"],"requires":["API key from PoseTracker account (freemium or paid)","HTTP client library (curl, fetch, requests, etc.)","Network connectivity to PoseTracker servers","Video encoding/decoding capability on client (to prepare frames for upload)"],"input_types":["multipart/form-data with video file (MP4, WebM, etc.)","raw image frame (JPEG, PNG)","base64-encoded image data in JSON"],"output_types":["JSON response with keypoint array, confidences, metadata","HTTP status codes (200 success, 400 bad request, 429 rate limit, 500 server error)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_posetracker-api__cap_4","uri":"capability://tool.use.integration.freemium.tier.api.access.with.usage.based.quota","name":"freemium tier api access with usage-based quota","description":"Provides free tier access to pose estimation with unspecified monthly or daily request limits, enabling developers to experiment and prototype before committing to paid plans. Quota enforcement likely implemented via API key rate limiting (requests per minute/hour) and monthly request caps. Freemium tier may have reduced model accuracy, longer inference latency, or lower priority in server queue compared to paid tiers.","intents":["I want to test pose tracking for my app idea without upfront costs","I'm building a prototype and need to validate product-market fit before investing in infrastructure","I need to understand pricing and performance characteristics before committing to a paid plan","I'm a student or hobbyist and can't afford commercial licensing"],"best_for":["indie developers and solo makers","students and academic researchers","teams prototyping MVP features","non-profit organizations with limited budgets"],"limitations":["Quota limits not published — unclear if freemium tier supports 100 requests/month or 10,000; no transparency on upgrade triggers","No documented SLA or uptime guarantee for freemium tier; service may be deprioritized during peak load","Potential inference latency penalty on freemium tier (e.g., queued behind paid requests); no published latency SLA","Unclear if freemium tier uses same model accuracy as paid tiers or a degraded variant","No apparent usage analytics or quota tracking dashboard; developers may hit limits unexpectedly"],"requires":["PoseTracker account registration (email + password or OAuth)","API key generation from account dashboard","Acceptance of terms of service (likely prohibiting commercial use on freemium tier)"],"input_types":["API requests (same as paid tier)"],"output_types":["pose data (same format as paid tier, potentially with latency penalty)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_posetracker-api__cap_5","uri":"capability://data.processing.analysis.pose.data.export.and.format.conversion.for.animation.software","name":"pose data export and format conversion for animation software","description":"Outputs pose keypoint data in formats compatible with animation tools (e.g., BVH, FBX, or proprietary game engine formats). Converts skeletal joint coordinates from PoseTracker's native representation into industry-standard motion capture formats, enabling direct import into Maya, Blender, Unreal Engine, or Unity. Likely includes bone hierarchy mapping, coordinate system transformation (e.g., Y-up to Z-up), and optional frame interpolation for smooth playback.","intents":["I want to import pose-tracked motion into Blender or Maya for character animation refinement","I need to export pose data to my game engine (Unity/Unreal) in a format it understands natively","I'm building a motion library and need to store pose sequences in a standard format for reuse","I want to blend pose-tracked motion with hand-animated sequences in my animation pipeline"],"best_for":["game developers integrating pose tracking into animation pipelines","motion designers and animators using pose capture as animation reference","studios building custom animation tools on top of pose data"],"limitations":["Export format support not documented — unclear if BVH, FBX, and game engine formats are all supported or only a subset","Coordinate system and bone hierarchy mapping may require manual adjustment for non-standard rigs","No apparent support for custom skeleton definitions — assumes standard 17–25 joint layout; non-standard rigs require manual remapping","Temporal interpolation quality not specified; exported motion may exhibit jitter or unnatural transitions between frames","No built-in IK (inverse kinematics) solving — exported data is forward kinematics only, may require post-processing in animation software"],"requires":["Animation software with import capability (Blender, Maya, game engine, etc.)","Understanding of bone hierarchy and coordinate systems in target software","Potentially custom scripts or plugins to map PoseTracker joints to target skeleton"],"input_types":["pose keypoint data (JSON or API response)"],"output_types":["BVH (Biovision Hierarchy) file","FBX (Autodesk) file","game engine-specific format (e.g., Unity animation clip, Unreal skeleton)","CSV or JSON with bone rotations and positions"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_posetracker-api__cap_6","uri":"capability://planning.reasoning.pose.driven.gesture.and.motion.pattern.recognition","name":"pose-driven gesture and motion pattern recognition","description":"Analyzes sequences of pose frames to recognize high-level gestures or motion patterns (e.g., 'jumping', 'waving', 'squatting') by matching joint trajectories against learned pattern templates. Likely uses temporal convolution or hidden Markov models to classify motion sequences, outputting gesture labels with confidence scores. Enables applications to respond to user actions (e.g., 'user performed a squat') rather than raw joint coordinates.","intents":["I want to detect when a user performs a specific exercise (squat, pushup, burpee) to count reps in a fitness app","I need to recognize hand gestures (thumbs up, peace sign) to control my game or app with pose","I'm building a dance game and need to detect if the user's motion matches a choreographed sequence","I want to classify user movement as 'active' vs 'idle' to trigger different app behaviors"],"best_for":["fitness app developers building rep-counting and form-analysis features","game developers building gesture-controlled interactions","dance and movement-based app creators","accessibility developers building pose-based control interfaces"],"limitations":["Gesture recognition accuracy not published — no benchmarks on false positive/negative rates across gesture types or user populations","Limited documentation on which gestures are pre-trained or supported; unclear if custom gesture training is available","Temporal window size for gesture detection not specified — unclear if system detects gestures in real-time (streaming) or requires buffering N frames","No apparent support for partial occlusion or non-standard body types — gesture recognition may fail if key joints are out of frame","Gesture vocabulary likely limited to common movements; specialized or niche gestures may not be recognized"],"requires":["Continuous pose frame stream (not single-frame snapshots)","Temporal buffering to accumulate frames for pattern matching","Knowledge of supported gesture vocabulary or custom training capability"],"input_types":["sequence of pose frames (temporal window of 10–100 frames depending on gesture duration)"],"output_types":["gesture label (string identifier)","confidence score (0.0–1.0)","gesture start/end frame indices","motion phase (e.g., 'descent' vs 'ascent' for squat)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_posetracker-api__cap_7","uri":"capability://automation.workflow.low.latency.pose.inference.for.interactive.real.time.applications","name":"low-latency pose inference for interactive real-time applications","description":"Optimizes inference pipeline for minimal end-to-end latency (capture → inference → output), targeting interactive use cases like live gaming or VR. Likely employs model quantization (INT8), pruning, or distillation to reduce computational cost, and may support edge deployment (on-device inference) for sub-50ms latency. Streaming inference mode processes frames as they arrive without buffering, enabling responsive pose-driven interactions.","intents":["I'm building a VR fitness game and need pose tracking with <100ms latency to avoid motion sickness","I want to use pose as a real-time game controller with minimal input lag","I'm streaming live pose data to an audience and need <500ms end-to-end latency for interactive overlays","I'm building a live dance game and need pose detection to sync with music in real-time"],"best_for":["VR and AR developers building immersive pose-driven experiences","game developers building real-time pose-controlled mechanics","live streaming content creators","interactive fitness app developers"],"limitations":["End-to-end latency not published — unclear if <100ms, <200ms, or <500ms is achievable; network latency dominates if using cloud API","Accuracy-latency tradeoff not documented — unclear if low-latency mode uses a degraded model with lower accuracy","No apparent on-device inference option for sub-50ms latency; cloud-based API inherently adds network round-trip time","Streaming mode behavior under variable frame rates or network jitter not specified","No published guidance on hardware requirements (GPU, CPU, RAM) for achieving target latency"],"requires":["Network connection with low jitter (for cloud API) or on-device GPU (if edge deployment available)","Video capture and frame encoding optimized for low latency (hardware encoding, minimal buffering)","Application architecture designed to handle variable latency (e.g., frame skipping, motion prediction)"],"input_types":["live video stream (WebRTC, RTMP, or raw frames)"],"output_types":["pose data with minimal buffering delay","latency metrics (inference time, network round-trip time)"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":45,"verified":false,"data_access_risk":"high","permissions":["Webcam or video file input (H.264, VP9, or raw RGB frames)","Network connectivity for API calls (unless edge deployment option exists)","Valid API key from PoseTracker freemium or paid tier","Client-side video capture capability (browser WebRTC, desktop app, or mobile SDK)","API response parsing capability in client code","Understanding of confidence score semantics (0.0 = no detection, 1.0 = high certainty)","Video input with consistent frame rate metadata","Client-side temporal buffering or streaming capability","Timestamp synchronization between video source and pose API","API key from PoseTracker account (freemium or paid)"],"failure_modes":["Single-person tracking only — no multi-person pose detection for group scenarios or sports analytics","Accuracy degrades significantly under poor lighting, extreme occlusion (limbs out of frame), or non-standard body types; no published variance metrics across conditions","Latency and accuracy characteristics at sustained high frame rates (60+ fps) not documented","No built-in temporal smoothing or filtering — raw keypoint jitter may require client-side post-processing","Requires clear frontal or near-frontal view; side/back angles may produce unreliable joint estimates","Confidence scores are model-relative and not calibrated to absolute accuracy — a 0.8 confidence joint may still be 5–10cm off in 3D space depending on camera angle and lighting","No published correlation between confidence score and actual positional error variance","Confidence does not account for temporal consistency — a joint can have high confidence in frame N and frame N+1 but represent physically impossible positions","Threshold tuning is empirical and application-specific; no guidance on recommended confidence cutoffs","No built-in temporal smoothing or interpolation — raw frame data may exhibit jitter between consecutive frames","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.7300000000000001,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:32.437Z","last_scraped_at":"2026-04-05T13:23:42.551Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=posetracker-api","compare_url":"https://unfragile.ai/compare?artifact=posetracker-api"}},"signature":"ABwPHbHMWH+4wt06InnzWSFFmSNCXjfP/y42qVNHkx77HJOLWsjR1vR2TRpBNO+uYdu2KF7Y2ek5fJivt2THCQ==","signedAt":"2026-06-22T12:39:08.057Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/posetracker-api","artifact":"https://unfragile.ai/posetracker-api","verify":"https://unfragile.ai/api/v1/verify?slug=posetracker-api","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}