--- title: "AI Motion" canonical: "https://aifitnessapi.com/motion" type: "cluster-index" pages: "13" last_reviewed: "2026-08-02" publisher: "AIFitnessAPI" --- # AI Motion > 13 pages. Each entry below shows the question the page owns, followed by its answer capsule. ## Pose Estimation Models Compared: MediaPipe, MoveNet, YOLO and More - Question: pose estimation models compared - HTML: https://aifitnessapi.com/motion/pose-estimation-models-compared - Markdown: https://aifitnessapi.com/motion/pose-estimation-models-compared.md - Last reviewed: 2026-07-24 For a single-user on-device fitness app, MediaPipe/BlazePose (33 keypoints, with monocular 3D world landmarks) is the usual starting point. MoveNet (17 keypoints) gives a speed-vs-accuracy dial for lightweight 2D tracking (Lightning for speed, Thunder for accuracy), while YOLO-pose and OpenPose handle multi-person scenes. The decisive trade-off is single-person on-device (private, cheap, simpler) vs multi-person (heavier, with real licensing strings). Check the license first: OpenPose is non-commercial and Ultralytics YOLO is AGPL-3.0. ## 2D vs 3D Pose Estimation for Fitness Apps - Question: 2d vs 3d pose estimation - HTML: https://aifitnessapi.com/motion/2d-vs-3d-pose-estimation - Markdown: https://aifitnessapi.com/motion/2d-vs-3d-pose-estimation.md - Last reviewed: 2026-07-24 2D pose estimation returns each keypoint as an (x, y) position in the image; 3D adds a depth axis (z) so joints sit in space. A normal RGB camera can output both, but its 3D depth is estimated from a single view (monocular), not measured, so it is less reliable than a multi-camera rig or depth sensor and weakest on occluded, out-of-plane, and extremity joints. Default to 2D for well-framed, in-plane form checks; add monocular 3D only when the movement leaves the camera plane, and treat depth as a lower-confidence axis you smooth and verify. ## Pose Estimation Accuracy: What It Means and What Drives It - Question: pose estimation accuracy - HTML: https://aifitnessapi.com/motion/pose-estimation-accuracy - Markdown: https://aifitnessapi.com/motion/pose-estimation-accuracy.md - Last reviewed: 2026-07-24 Pose estimation accuracy is not one number. It is a set of metrics measured on a specific dataset: PCK and OKS for 2D keypoints, MPJPE for 3D joints. Real-world accuracy is driven down by lighting, occlusion, camera angle, distance, loose clothing, fast motion, and multiple people, and it is recovered with per-keypoint confidence gating and temporal smoothing like the One-Euro filter. The key takeaway: a leaderboard score is not your app's accuracy, so the only number that matters is the one you measure on your own footage. ## Multi-Person Pose Tracking: Top-Down vs Bottom-Up - Question: multi person pose tracking - HTML: https://aifitnessapi.com/motion/multi-person-pose-tracking - Markdown: https://aifitnessapi.com/motion/multi-person-pose-tracking.md - Last reviewed: 2026-07-24 Multi-person pose tracking estimates several people's skeletons in one frame and keeps each identity stable across frames. Two paradigms exist: top-down detects each person then runs pose per box (accurate per person, but cost grows with the number of people), and bottom-up finds all keypoints then groups them (cost stays roughly constant, but grouping is harder and less accurate). Most fitness apps are single-user, where single-person models like MediaPipe or MoveNet are simpler and more accurate. Pick multi-person (YOLO-pose top-down or OpenPose bottom-up, plus a cross-frame tracking layer) only for group classes, gyms, or two-person sessions. ## On-Device vs Cloud Pose Estimation: Which to Choose - Question: on device vs cloud pose estimation - HTML: https://aifitnessapi.com/motion/on-device-vs-cloud-pose-estimation - Markdown: https://aifitnessapi.com/motion/on-device-vs-cloud-pose-estimation.md - Last reviewed: 2026-07-24 The choice is where the pose model runs: on the phone or on a server. For consumer fitness, on-device is almost always the right default - toolkits like ML Kit Pose, MediaPipe/BlazePose, TensorFlow Lite/LiteRT, and Core ML or Vision run inference locally, so it is private, works offline, has no network round-trip, and costs nothing per frame. Cloud lets you run a heavier or custom model with results consistent across every device, but you pay in latency, bandwidth, per-frame cost, and the privacy weight of streaming raw workout video. Pick on-device for real-time coaching and privacy; reach for cloud only when a model will not fit on the phone or you need identical output across many devices. ## Real-Time Pose Estimation: Frame Rate, Latency, and Model Trade-offs - Question: real time pose estimation - HTML: https://aifitnessapi.com/motion/real-time-pose-estimation - Markdown: https://aifitnessapi.com/motion/real-time-pose-estimation.md - Last reviewed: 2026-07-24 Real-time pose estimation means the pipeline keeps up with the live camera stream so feedback feels immediate. It is a budget problem: a frame-rate and latency target balanced against model size, battery, and thermals. The central lever is model size vs speed. Pick a smaller, faster model (MoveNet Lightning or a fast SDK mode) for live rep counting and low-end devices; pick a larger, more accurate one (MoveNet Thunder or an accurate mode) for detailed form scoring where you can process fewer frames. Treat every fps and latency figure as device-dependent and verify on your target hardware. ## Pose Estimation Hardware Requirements: What You Actually Need - Question: pose estimation hardware requirements - HTML: https://aifitnessapi.com/motion/pose-estimation-hardware-requirements - Markdown: https://aifitnessapi.com/motion/pose-estimation-hardware-requirements.md - Last reviewed: 2026-07-24 A normal RGB smartphone camera is enough for both 2D pose and monocular (single-camera) 3D pose - no depth sensor or LiDAR is required. What matters is hardware acceleration: a GPU or NPU/Neural Engine keeps inference fast and battery-friendly, while older and low-end devices with weaker chips run slower and may need a lighter model, lower resolution, or frame dropping. Lighting and full-body framing affect accuracy as much as silicon does. Best pick for reach: target the RGB camera plus a GPU delegate so mid-range and older phones work, and opt into NPU/Neural Engine for headroom where it exists. Going cloud instead adds a server GPU as a recurring cost. ## How Rep Counting Works: The Algorithm Explained - Question: how does rep counting work - HTML: https://aifitnessapi.com/motion/how-rep-counting-works - Markdown: https://aifitnessapi.com/motion/how-rep-counting-works.md - Last reviewed: 2026-07-24 Rep counting reduces a pose to one signal over time — usually a joint angle like the elbow (shoulder-elbow-wrist) for a curl — and counts a rep each time that signal completes a full up-down cycle. Two approaches dominate: peak/valley detection on the angle trajectory, or a finite state machine that models up/down phases with thresholds. The key to not double-counting jitter is hysteresis: separate entry thresholds for the up and down phases so noise near one boundary cannot re-fire. A state machine with hysteresis, run on a smoothed signal, is the robust default; pure peak detection is simpler but needs smoothing and a minimum-amplitude gate. ## How Camera-Based Form Feedback Works - Question: how does form feedback work - HTML: https://aifitnessapi.com/motion/how-form-feedback-works - Markdown: https://aifitnessapi.com/motion/how-form-feedback-works.md - Last reviewed: 2026-07-24 Camera-based form feedback computes the angle at a joint from three tracked keypoints, watches that angle across a rep to capture range of motion, and compares it against a reference or target. When the user drifts outside a tolerance band, the app cues which way to correct. It runs from an ordinary phone camera with no depth sensor, but the pose underneath is a monocular estimate, so it is a coaching aid, not medical or physical-therapy advice. ## Build vs Buy: AI Motion Tracking - Question: build vs buy motion tracking - HTML: https://aifitnessapi.com/motion/build-vs-buy-ai-motion-tracking - Markdown: https://aifitnessapi.com/motion/build-vs-buy-ai-motion-tracking.md - Last reviewed: 2026-07-24 Building your own AI motion pipeline means picking a pose model, integrating it natively per platform, and writing all the rep-counting, form-scoring, and exercise-library logic yourself, since keypoints are only coordinates. Buying a motion-tracking SDK bundles pose plus pre-built fitness logic, vendor-maintained, so you ship faster at a recurring per-user cost and with less control. Build when camera-based tracking is your core differentiator and you have CV/ML staff; buy when time-to-market and cross-platform maintenance matter more. Most teams go hybrid: adopt an on-device pose model, build the coaching layer on top. ## MediaPipe vs MoveNet: Decide by What You Compute Downstream - Question: movenet vs mediapipe - HTML: https://aifitnessapi.com/motion/mediapipe-vs-movenet - Markdown: https://aifitnessapi.com/motion/mediapipe-vs-movenet.md - Last reviewed: 2026-08-02 Decide by what you compute from the keypoints, not by benchmark screenshots. MediaPipe/BlazePose outputs 33 landmarks plus estimated 3D world landmarks, which is what joint-angle form feedback needs; MoveNet outputs 17 COCO keypoints in 2D, with the Lightning variant tuned for minimal latency, which is all a rep counter on modest hardware needs. MoveNet MultiPose is the only verified multi-person option in this pair. Both are Apache-2.0, both are effectively frozen, and every published speed number is a vendor claim you must re-measure on your own devices. ## MediaPipe Pose Landmarker Models: Lite vs Full vs Heavy - Question: pose landmarker lite vs full vs heavy - HTML: https://aifitnessapi.com/motion/mediapipe-pose-landmarker-models - Markdown: https://aifitnessapi.com/motion/mediapipe-pose-landmarker-models.md - Last reviewed: 2026-08-02 MediaPipe Pose Landmarker ships as three .task bundles - lite, full, and heavy - that share the same 33-landmark output and API, so the variant is a swappable config value, not an architecture decision. What differs is the size-speed-accuracy trade: Google's model card positions lite as the only variant near real-time on a modest CPU, heavy as the most accurate at a fraction of the frame rate, with full in between. Start with full for form feedback and lite for live rep counting, then verify on your own hardware - the published numbers come from a 2021 model card measured on a Pixel 3. ## Apple Vision Framework Body Pose: The Native iOS Option - Question: apple vision framework body pose - HTML: https://aifitnessapi.com/motion/apple-vision-body-pose - Markdown: https://aifitnessapi.com/motion/apple-vision-body-pose.md - Last reviewed: 2026-08-02 Apple's Vision framework gives you two body pose requests with no model file to ship: VNDetectHumanBodyPoseRequest (2D, 19 named joints, iOS 14+) and VNDetectHumanBodyPose3DRequest (3D, 17 named joints with camera-relative positions and a metric body-height estimate, iOS 17+). Choose Vision for an iOS-only app that wants zero model bytes, OS-maintained inference, and built-in offline video via VNVideoProcessor; choose a bundled model like MediaPipe or MoveNet when an Android sibling app exists or you need to pin a model version for regression testing. The catch to weigh honestly: Apple publishes no model card and no accuracy numbers, so any accuracy claim about Vision is unverifiable — measure it on your own footage, not from a spec sheet.