Many computer vision techniques infer properties of our physical world from images.
Although images are formed through the physics of light and mechanics, computer vision …

Nowadays, robots are dominating the manufacturing, entertainment, and healthcare
industries. Robot vision aims to equip robots with the capabilities to discover information …

Building a robot that can understand and learn to interact by watching humans has inspired
several vision problems. However, despite some successful results on static datasets, it …

Understanding how humans use physical contact to interact with the world is key to enabling
human-centric artificial intelligence. While inferring 3D contact is crucial for modeling …

Humans live within a 3D space and constantly interact with it to perform tasks. Such
interactions involve physical contact between surfaces that is semantically meaningful. Our …

Humans constantly contact objects to move and perform tasks. Thus, detecting human-
object contact is important for building human-centered artificial intelligence. However, there …

Synthesizing 3D human motion plays an important role in many graphics applications as
well as understanding human activity. While many efforts have been made on generating …

Recent progress in deep learning is essentially based on a “big data for small tasks”
paradigm, under which massive amounts of data are used to train a classifier for a single …

We present a human-centric method to sample and synthesize 3D room layouts and 2D
images thereof, for the purpose of obtaining large-scale 2D/3D image data with the perfect …

Learning how to interact with objects is an important step towards embodied visual
intelligence, but existing techniques suffer from heavy supervision or sensing requirements …