摘要
虚拟人运动合成是虚拟现实和角色动画领域中的关键问题之一,旨在合成真实自然且能够响应用户输入信息的运动序列.虚拟人运动控制策略根据用户输入约束解算关节力矩,并依托现有的物理引擎更新虚拟人状态,合成的运动序列在满足用户输入约束的同时可以满足物理真实性.近年来,深度强化学习技术因在序列决策和交互任务中的出色表现而备受研究者的关注,为基于物理引擎的虚拟人控制策略学习提供了新途径.文中对虚拟人运动控制策略学习方法进行综述,从理论基础和应用设计等方面介绍相关研究.在应用设计方面,首先基于深度强化学习的基础元素,从状态表示、奖励函数设计、控制策略设计以及物理仿真4个角度对现有工作进行梳理总结;其次,分析现有通用技术框架并指出其在控制策略上的拓展方向,并以实际问题为例探讨虚拟人运动控制策略的具体应用.最后,总结当前该领域的研究现状,指出利用丰富的运动捕获数据提升运动控制策略的深度与广度是未来的主要研究方向,展望虚拟人运动控制策略学习方法在多模态的感知与控制、世界模型学习和具身智能等应用方向的发展前景.
Motion synthesis for humanoid characters,aimed at generating realistic and natural motion sequences that can respond to user input,has long been a formidable challenge in the fields of virtual reality and character animation.Motion control policies address this challenge by calculating joint torques based on user input constraints,updating the character state using existing physics engines,and synthesizing motion sequences that not only meet user input constraints but also ensure physical realism.In recent years,deep reinforcement learning has garnered significant attention from researchers due to its exceptional performance in sequential decision-making and interactive tasks,providing a novel approach for learning control policies for humanoid characters grounded in physics engines.This paper reviews the advancements in motion control policy learning for humanoid characters and introduces relevant research from both theoretical foundations and practical design perspectives.In terms of practical designs,existing works are examined from four key aspects:state representation,reward function design,control policy design,and the physical simulation engine employed,all based on the fundamental elements of deep reinforcement learning.Furthermore,a comprehensive analysis of a general technical framework is conducted,highlighting potential directions for extending control policies.The specific application of motion control policies for humanoid characters is discussed using practical problems as case studies.Finally,a summary of the current research status is provided,indicating that leveraging extensive motion capture data to enhance the depth and breadth of motion control policies represents a promising future research direction.The paper also outlines the prospects for the development of motion control policy learning for humanoid characters,particularly in the areas of multimodal perception and control,world model learning,and embodied intelligence.
作者
叶永竞
许逸文
张子豪
胡磊
夏时洪
Ye Yongjing;Xu Yiwen;Zhang Zihao;Hu Lei;Xia Shihong(Prospective Research Laboratory,Institute of Computing Technology,Chinese Academy of Sciences,Beijing 1001902;University of Chinese Academy of Sciences,Beijing 100049)
出处
《计算机辅助设计与图形学学报》
北大核心
2025年第2期185-206,共22页
Journal of Computer-Aided Design & Computer Graphics
基金
工业软件重点专项(2022YFB3303202)
国家自然科学基金(62302481)。
关键词
虚拟人
运动控制策略
强化学习
深度学习
角色动画
humanoid character
motion control policy
reinforcement learning
deep learning
character anima-tion
作者简介
叶永竞(1995-),男,博士研究生,主要研究方向为计算机图形学、虚拟现实;许逸文(1997-),男,博士研究生,主要研究方向为计算机图形学、虚拟现实;张子豪(1993-),男,博士,助理研究员,主要研究方向为计算机图形学、计算机视觉;胡磊(1997-),男,博士研究生,主要研究方向为计算机图形学、三维人体运动建模与生成;通信作者:夏时洪(1974-),男,博士,研究员,博士生导师,CCF高级会员,主要研究方向为计算机图形学、虚拟现实、人工智能.xsh@ict.ac.cn。
