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This work in progress paper presents and motivates the design of a novel extended reality (XR) environment for artificial intelligence (AI) education, and presents its first implementation. The learner is seated at a table and wears an XR headset that allows them to see both the real world and a visualization of a neural network. The visualization is adjustable. The learner can inspect each layer, each neuron, and each connection. The learner can also choose a different input image, or create their own image to feed to the network. The inference is computed on the headset, in real time. The neural network configuration and its weights are loaded from an onnx file, which supports a variety of architectures as well as changing the weights to illustrate the training process.
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null (Ed.)We introduce a new inverse modeling method to interactively design crowd animations. Few works focus on providing succinct high-level and large-scale crowd motion modeling. Our methodology is to read in real or virtual agent trajectory data and automatically infer a set of parameterized crowd motion models. Then, components of the motion models can be mixed, matched, and altered enabling rapidly producing new crowd motions. Our results show novel animations using real-world data, using synthetic data, and imitating real-world scenarios. Moreover, by combining our method with our interactive crowd trajectory sketching tool, we can create complex spatio-temporal crowd animations in about a minute.more » « less
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null (Ed.)We introduce a novel tool for designing a swarming behavior model for a set of virtual agents to automatically capture an initially unknown indoor architectural environment. Our key idea is to use an output-driven optimization to create targeted swarming behavior. The input to our model is a simple rectangular proxy of the target area and desired acquisition indicator values. The final outputs are the parameters for a swarming behavior model that is autonomous and decentralized, uses only local exploration, and is robust to agent failure. We show and compare the swarming performance in several simulated environments of up to several hundred square meters, 100 agents, and under various conditions.more » « less
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