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 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 …

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 GANimator, a generative model that learns to synthesize novel motions from a
single, short motion sequence. GANimator generates motions that resemble the core …

Reinforcement learning (RL) has emerged as a powerful approach for locomotion control of
highly articulated robotic systems. However, one major challenge is the tedious process of …

Interactive, task-guided character controllers must be agile and responsive to user input,
while retaining the flexibility to be readily authored and modified by the designer. Central to …

This paper introduces a new generative statistical model that allows for human motion
analysis and synthesis at both semantic and kinematic levels. Our key idea is to decouple …

We propose a novel representation of motion data and control that enables characters with
both highly agile responses to user input and natural handling of arbitrary external …

Motion completion is a challenging and long-discussed problem, which is of great
significance in film and game applications. For different motion completion scenarios (in …

Real-time in-between motion generation is universally required in games and highly
desirable in existing animation pipelines. Its core challenge lies in the need to satisfy three …