Gestures that accompany speech are an essential part of natural and efficient embodied
human communication. The automatic generation of such co‐speech gestures is a long …

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

Learning the spatial-temporal structure of body movements is a fundamental problem for
character motion synthesis. In this work, we propose a novel neural network architecture …

Human motion prediction aims to forecast future human poses given a past motion. Whether
based on recurrent or feed-forward neural networks, existing methods fail to model the …

A longstanding goal in character animation is to combine data-driven specification of
behavior with a system that can execute a similar behavior in a physical simulation, thus …

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 …

In this work we present a novel, robust transition generation technique that can serve as a
new tool for 3D animators, based on adversarial recurrent neural networks. The system …

We present a framework to synthesize character movements based on high level
parameters, such that the produced movements respect the manifold of human motion …

We study the problem of realizing the full spectrum of bipedal locomotion on a real robot with
sim-to-real reinforcement learning (RL). A key challenge of learning legged locomotion is …

Quadruped motion includes a wide variation of gaits such as walk, pace, trot and canter, and
actions such as jumping, sitting, turning and idling. Applying existing data-driven character …