Synthetic visual data can provide practically infinite diversity and rich labels, while avoiding ethical issues with privacy and bias.

Frequently,transformations occurring in data can be better represented by a subset of a group than by agroup asawhole, e.g., rotations in[ 90,90 ]. Insuch cases, amodel that respects equivariancepartially is better suited to represent the data.

It comes with ground truth 3D pose and follow its standard split for training and testing. MannequinChallenge is captured by a moving hand-held camera while subjects stay still in daily life scenarios. We reconstruct 3D body shape and pose using multiview images from those datasets and report MPJPE-PAmetricinTable1. We test run time for a different number of views within a frame. The graph convolutional mesh decoder has 7 layers, each with 256 channels.

Event cameras are novel bio-inspired sensors that offer advantages over traditional cameras (low latency, high dynamic range, low power, etc.). Optical flow estimation methods that work on packets of events trade off speed for accuracy, while event-by-event (incremental) methods have strong assumptions and have not been tested on common benchmarks that quantify progress in the field. Towards applications on resource-constrained devices, it is important to develop optical flow algorithms that are fast, light-weight and accurate. This work leverages insights from neuroscience, and proposes a novel optical flow estimation scheme based on triplet matching. The experiments on publicly available benchmarks demonstrate its capability to handle complex scenes with comparable results as prior packet-based algorithms. In addition, the proposed method achieves the fastest execution time (> 10 kHz) on standard CPUs as it requires only three events in estimation. We hope that our research opens the door to real-time, incremental motion estimation methods and applications in real-world scenarios.

In this episode, Jack Rasiel speaks with Kostas Daniilidis, Professor of Computer and Information at the University of Pennsylvania, about new developments in computer vision and robotics. Daniilidis' research team is pioneering new approaches to understanding the 3D structure of the world from simple and ubiquitous 2D images. They are also investigating how these techniques can be used to improve robots' ability to understand and manipulate objects in their environment. Daniilidis puts this in the context of current trends in robot learning and perception, and speculates how it will help bring more robots from the lab to the "real world". How does bleeding edge research become a viable product? Daniilidis speaks to this from personal experience, as an advisor to startups spun out from the GRASP Lab and Penn's Pennovation incubator. Kostas Daniilidis is the Ruth Yalom Stone Professor of Computer and Information Science at the University of Pennsylvania where he has been faculty since 1998.

Computer scientists at MIT and the National University of Ireland (NUI) at Maynooth have developed a mapping algorithm that creates dense, highly detailed 3-D maps of indoor and outdoor environments in real time. The researchers tested their algorithm on videos taken with a low-cost Kinect camera, including one that explores the serpentine halls and stairways of MIT's Stata Center. Applying their mapping technique to these videos, the researchers created rich, three-dimensional maps as the camera explored its surroundings. As the camera circled back to its starting point, the researchers found that after returning to a location recognized as familiar, the algorithm was able to quickly stitch images together to effectively "close the loop," creating a continuous, realistic 3-D map in real time. The technique solves a major problem in the robotic mapping community that's known as either "loop closure" or "drift": As a camera pans across a room or travels down a corridor, it invariably introduces slight errors in the estimated path taken.