Product Introduction

Semcam Live is an edge AI markerless motion capture system. Users do not need mocap suits, inertial sensors, or reflective markers to capture motion in a natural state.

The system is designed for multi-camera capture, real-time applications, and long-term deployment in life sciences, university laboratories, robotics, simulation training, large-space interaction, animation, and visual effects.

Product Positioning

Semcam Live is not a tool that only estimates motion from a single video clip. It is a complete system covering device management, camera calibration, real-time capture, recording and playback, data export, and extended analysis.

Its edge computing architecture lets Semcam AI cameras analyze images on the device first, while ActiveCenter performs multi-camera synchronization, reconstruction, visualization, and output. This division reduces network pressure and makes the system easier to scale to larger camera arrays and capture spaces.

System Modules

Product	Main role
Semcam AI Camera	Processes images at the camera and provides markerless human motion data
ActiveCenter	Manages devices and calibration, then reconstructs, previews, records, plays back, and exports motion
HPE high-precision processing	Runs offline computation after capture to produce higher-quality human motion results
Goku optical camera	Tracks rigid bodies, props, robot end effectors, training tools, or other marked objects

Force plates, EMG, timecode devices, Active Biomechanics, and motion quality assessment can also be configured when a project requires them.

Core Advantages

Natural, low-interference capture

Participants do not need dedicated wearable devices. This shortens preparation time and reduces the influence of equipment on gait, training, rehabilitation, multi-person interaction, and repeated data collection.

Edge computing and local deployment

Cameras process part of the visual workload first and send only the data needed for motion capture to ActiveCenter. Compared with sending every raw video stream to one computer, this approach reduces network traffic, shortens the real-time path, scales compute with the number of cameras, reduces centralized raw-video storage, and is better suited to local, long-running sites.

Real-time and high-precision workflows

Operators can view body rigs, rigid bodies, and trajectories on site and stream results to downstream software. For tasks that prioritize quality, HPE can process selected recordings after capture and use reference-camera images for review.

Hybrid human and object capture

Semcam captures markerless human motion, while Goku tracks rigid bodies and marked objects. Together they can record people, props, robots, training equipment, and virtual cameras in one project.

Typical Workflow

1. Connect cameras and external devices.
2. Calibrate cameras and define the capture space.
3. Create human or rigid-body capture targets.
4. Preview results in real time and start recording.
5. Play back, reprocess, or run high-precision computation.
6. Stream or export results to the target software.

For a fuller view of the data path, see System Architecture.

Difference from Ordinary AI Video Mocap

Topic	Semcam Live	Ordinary single-video AI mocap
Main use	Professional capture, real-time use, and long-term deployment	Quick motion generation from video
Capture	Synchronized multi-camera system with scalable capture space	One or a few video files
Processing	Camera-side processing plus center-side fusion	Usually centralized after capture
Real time	Real-time preview and data output	Mostly offline results
Object tracking	Can work with Goku for rigid bodies and props	Usually human-body only
Data management	Recording, playback, reprocessing, and multiple outputs	Usually final motion files only

Configuration Boundaries

The final system should be configured around capture space, number of people, motion type, accuracy, real-time requirements, target software, and site network. Device capabilities, interfaces, formats, and extensions are subject to the purchased model, software version, licensed modules, and project acceptance results.