Learning physics-based character controllers that can successfully integrate diverse motor
skills using a single policy remains a challenging problem. We present a system to learn …

Despite the growing demand for capturing diverse motions, collecting climbing motion data
remains challenging due to difficulties in tracking obscured markers and scanning climbing …

Animation and machine learning research have shown great advancements in the past
decade, leading to robust and powerful methods for learning complex physically-based …

This paper addresses the problem of synthesizing simulated humanoid climbing movements
given the target holds, eg, by the player of a climbing game. We contribute the first deep …

We propose a novel method for exploring the dynamics of physically based animated
characters, and learning a task-agnostic action space that makes movement optimization …

Autonomous virtual agents have been employed in different areas, spanning applications
from education and training to gaming and e-commerce. In particular, agents of human-like …

Although there is an increasing demand for capturing various natural motions, collecting
climbing motion data is difficult due to technical complexities, related to obscured markers …

요약 최근 자연스러운 모션 데이터에 대한 수요가 늘고 있지만, 클라이밍 모션을 정확하게
캡처하는 것은 가려진 부분이 많은 클라 이밍 동작의 특성상 쉽지 않다. 또한 벽 구조물의 …

Many successful examples of learning and optimizing simulated movement rely on some
form of curriculum, ie, progressions from easy to hard tasks. Although curriculum learning …