The metaverse is a visual world that blends the physical world and digital world. At present,
the development of the metaverse is still in the early stage, and there lacks a framework for …

Learning physics-based locomotion skills is a difficult problem, leading to solutions that
typically exploit prior knowledge of various forms. In this paper we aim to learn a variety of …

The history of learning for control has been an exciting back and forth between two broad
classes of algorithms: planning and reinforcement learning. Planning algorithms effectively …

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 …

We present a real-time character control mechanism using a novel neural network
architecture called a Phase-Functioned Neural Network. In this network structure, the …

Planning algorithms are impacting technical disciplines and industries around the world,
including robotics, computer-aided design, manufacturing, computer graphics, aerospace …

Research into the methods and techniques used in simulating crowds has developed
extensively within the last few years, particularly in the areas of video games and film …

We propose a framework, called Lightning, for planning paths in high-dimensional spaces
that is able to learn from experience, with the aim of reducing computation time. This …

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 …

Many compelling applications would become feasible if novice users had the ability to
synthesize high quality human motion based only on a simple sketch and a few easily …