Current human activity detection and recognition techniques rely largely on traditional electro-optical/infra-red (EO/IR) imagery data, but there are inherent limitations and complexities in analyzing human-related EO/IR data and models. Fusing non-EO/IR sensors, such as Light Detection and Ranging (LIDAR) and Synthetic Aperture Radar (SAR), and developing a sufficient-fidelity simulation tool to create multi-sensor datasets for human activity detection and recognition would be very useful. IAI and collaborators will develop a novel, integrated, sufficient-fidelity simulation tool called HumanView (HumV). This tool will generate synthetic multimodal non-EO/IR sensor returns for human activities, with mathematically modeled sensor physics and real-world environment effects. The software will leverage existing tools, and integrate commercial, proprietary and commercial-off-the-shelf (COTS) multi-physics simulation tools and material properties databases. The architecture will include a HumV Models module, with a data store containing 3D human models, 3D geometric models of the environment, relevant sensor models for RF, LIDAR and Hyperspectral, and platform models of Unmanned Aerial Vehicles and aircraft on which the sensors reside. The HumV editor module will allow researchers to discover available models, manage the configuration associated with a model using a drag-and-drop interface, create specific activities for the model, and associate mathematical models where appropriate. The HumV simulator module will allow users to simulate multiple scenarios and generate synthetic sensor data for analytics. HumV technology can help generate realistic and physically sound sensor imagery data from virtual 3D scenes and will be useful to the military, in homeland security analysis, and for developing first responder support equipment in search and rescue operations. It would also be useful for algorithm development in applications like self-driving cars, and other Artificial Intelligence or robotic systems that interact with humans.