Multimodal & Spatial Computing · core
Pose & Motion Tracking
Landmarks, skeletons, optical flow, temporal smoothing, identity tracking, calibration, occlusion, and latency.
Mental model
Pose tracking is temporal estimation under ambiguity. Combine per-frame evidence with identity, motion priors, smoothing, and explicit confidence through occlusion.
How to study Pose & Motion Tracking
Begin by restating the mental model in your own words, then connect it to a concrete system you have built or operated. Name the mechanism, the constraint it addresses, and the trade-off it introduces. Use BlazePose: On-device Real-time Body Pose Tracking, Stacked Hourglass Networks for Human Pose Estimation, OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields to check details, but close the source before writing your explanation. Retrieval is the learning step; rereading is only preparation.
Next, compare Pose & Motion Tracking with Voice & Audio Systems, Image & Video Generation. Ask what changes in correctness, latency, resource use, operability, and failure recovery. Complete Design exercise: Pose & Motion Tracking and preserve the command, input, output, and one failed attempt as evidence. Finish by explaining the idea without jargon to someone who has not studied the track.
Proof of understanding
- Explain the mechanism from first principles and identify the state it reads or changes.
- Give one situation where the concept is the right choice and one where it is not.
- Predict a realistic failure mode before running the drill, then compare the prediction with evidence.
- Connect the result to a roadmap or build artifact instead of treating the concept as isolated trivia.
Learn from primary sources
- BlazePose: On-device Real-time Body Pose Tracking (doc)
- Stacked Hourglass Networks for Human Pose Estimation (doc)
- OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields (paper)
- CMU Panoptic Studio — Massively Multiview Social Motion Capture (article)
- MediaPipe Pose Landmarker (doc)
Practice and explain it back
Design exercise: Pose & Motion Tracking
Landmarks, skeletons, optical flow, temporal smoothing, identity tracking, calibration, occlusion, and latency. Implement designOutline() returning non-empty values for: landmarks, temporalTracking, occlusionHandling. Each value must name a concrete mechanism or decision.
Expected evidence: A design outline with landmarks, temporalTracking, occlusionHandling plus an explicit failure mode or trade-off.
Open the interactive drill →Review prompts
- Per-frame landmark detection is accurate but the skeleton jitters. What is happening and what is the tradeoff in fixing it?
Build evidence
Synthesize: Multimodal & Spatial Computing
Connect vision, audio, generation, on-device intelligence, robotics, spatial interfaces, and HCI. Produce one working system, benchmark, or evidence-backed design that integrates the path.
- Implements or precisely models the core mechanisms from all three milestones
- Includes at least one injected failure or adversarial case and demonstrates recovery
- Reports quality, latency, resource, reliability, or usability measurements relevant to the domain
- Ships a concise architecture note explaining decisions, trade-offs, and remaining risks