Reinforcement Learning is an area of Machine Learning focused on how agents can be
trained to make sequential decisions, and achieve a particular goal within an arbitrary …

Physics-based simulation provides an accelerated and safe avenue for developing,
verifying, and testing robotic control algorithms and prototype designs. In the quest to …

The incredible feats of athleticism demonstrated by humans are made possible in part by a
vast repertoire of general-purpose motor skills, acquired through years of practice and …

Synthesizing graceful and life-like behaviors for physically simulated characters has been a
fundamental challenge in computer animation. Data-driven methods that leverage motion …

A fundamental problem in computer animation is that of realizing purposeful and realistic
human movement given a sufficiently-rich set of motion capture clips. We learn data-driven …

Marker-less 3D human motion capture from a single colour camera has seen significant
progress. However, it is a very challenging and severely ill-posed problem. In consequence …

We present a new trainable system for physically plausible markerless 3D human motion
capture, which achieves state-of-the-art results in a broad range of challenging scenarios …

Human character animation is often critical in entertainment content production, including
video games, virtual reality or fiction films. To this end, deep neural networks drive most …

Interactively synthesizing novel combinations and variations of character movements from
different motion skills is a key problem in computer animation. In this paper, we propose a …

Existing deep models predict 2D and 3D kinematic poses from video that are approximately
accurate, but contain visible errors that violate physical constraints, such as feet penetrating …