Research is shedding light on how autonomous systems can foster human confidence in robots. Largely, the research suggests that humans have an easier time trusting a robot that offers some kind of self-assessment as it goes about its tasks.
From Justus Thies, Michael Zollhöfer, Marc Stamminger, Christian Theobalt and Matthias Nießner:
We present a novel approach for real-time facial reenactment of a monocular target video sequence (e.g., Youtube video). The source sequence is also a monocular video stream, captured live with a commodity webcam. Our goal is to animate the facial expressions of the target video by a source actor and re-render the manipulated output video in a photo-realistic fashion. To this end, we first address the under-constrained problem of facial identity recovery from monocular video by non-rigid model-based bundling. At run time, we track facial expressions of both source and target video using a dense photometric consistency measure. Reenactment is then achieved by fast and efficient deformation transfer between source and target. The mouth interior that best matches the re-targeted expression is retrieved from the target sequence and warped to produce an accurate fit. Finally, we convincingly re-render the synthesized target face on top of the corresponding video stream such that it seamlessly blends with the real-world illumination... (full paper)
Advances in AI and deep machine learning have ushered in incredible potential for ground-based, aerial, and maritime robotics. Robotics is moving from an opportunity in business and facility operations to a necessity in many industries. As a result, organizations must plan for multi-vendor robots, intelligent traffic flows, storage, and more.