Stanford researchers have created a single diffusion generative model, DiffusionPoser, that can reconstruct human motion in real-time from arbitrary body sensor configurations, with broad application in a variety of motion capture end uses.
Researchers in the Murmann Mixed Signal Group have developed a pipelined chip architecture with inverted residual and linear bottlenecks-based networks for energy efficient Machine Learning inference on edge devices.