A research institution in South Carolina is seeking a markerless motion capture system to enable high-precision three-dimensional biomechanical analysis. The required system must utilize advanced machine learning technologies to extract accurate 3D pose and biomechanical data from synchronized multi-camera video without the use of physical markers. This approach is designed to facilitate naturalistic movement capture in real-life–like environments, ensuring both ecological validity and research-grade data quality.

The system must eliminate the need for manual participant preparation and model calibration, which are particularly challenging when working with neurologically impaired populations with variable motor control and movement patterns. It should offer proven validation in peer-reviewed scientific literature and produce high-precision, quantitative biomechanical outputs suitable for rigorous research analysis. Compatibility with commercially available motion capture and data processing workflows is required, as is flexibility to operate with cameras from multiple manufacturers for future expansion and customization.

Deployment should be rapid with streamlined setup and configuration in laboratory environments. Solutions relying on markers or requiring extensive custom development or integration will not be considered. Interested vendors may submit questions about the project until February 10, 2026.