Creating and retargetting motion by the musculoskeletal human body model

Recently, optimization has been used in various ways to interpolate or retarget human body motions obtained by motion-capturing systems. However, in such cases, the inner structure of a human body has rarely been taken into account, and hence there have been difficulties in simulating physiological effects such as fatigue or injuries. In this paper, we propose a method to create/retarget human body motions using a musculoskeletal human body model. Using our method, it is possible to create dynamically and physiologically feasible motions. Since a muscle model based on Hill's model is included in our system, it is also possible to retarget the original motion by changing muscular parameters. For example, using the muscle fatigue model, a motion where a human body gradually gets tired can be simulated. By increasing the maximal force exertable by the muscles, or decreasing it to zero, training or displacement effects of muscles can also be simulated. Our method can be used for biomechanically correct inverse kinematics, interpolation of motions, and physiological retargetting of the human body motion.  

A Comparative analysis of Green’s functions of 1D matching equations for motion synthesis

When filtering an input image, the Green’s functions of matching equations are capable of inducing a broad class of motions, a property that has led to their use in several computer graphics and computer vision applications. In all such applications, the Green’s functions of second-order differential equations have been considered, even though no justification has been given for their preference over simpler, first-order equations. Here we present a study of first-order one-dimensional matching equations, both in the uniform and in the affine motion models. Comparing their Green’s functions with those of the corresponding second-order cases, we find evidence for the latter’s superiority in motion synthesis. We also propose and discuss a general discretization scheme for Green’s functions of one-dimensional matching equations, showing that the affine motion model is particularly sensitive to the sampling frequency. In this case, we advocate the use of area sampling, for allowing realistic motion simulations.  

运动路径驱动的角色动画合成方法

为了给虚拟角色合成出逼真的大范围运动数据,提出一种为特定运动路径配上自然多样的人体运动数据的方法.该方法将待匹配运动路径分割为一系列短小的运动路径片断,并在一个结构化的运动状态机上逐片断地寻找最为匹配的运动序列;然后将这些运动序列依次连接起来,得到一段连续的、满足运动路径约束的人体运动数据.用户还可以交互式地设计运动路径上一个路径片断的运动类型.该方法适用于待匹配运动路径运动范围大、对多条运动路径同时计算匹配运动、多条运动路径之间交叉频繁的情景.  

3维人体运动编辑与合成技术综述

为了使人们对3维人体运动编辑与合成技术有个概略了解，对当前3维人体运动编辑和合成技术的研究现状进行了系统阐述。首先对4大类3维人体运动合成方法进行了分析比较；然后按照运动捕获数据的表示方法、运动编辑技术、运动合成技术3个方面着重分析了运动捕获数据驱动的运动合成方法的发展现状；最后对目前3维人体运动编辑与合成技术研究中的难点问题及未来研究重点进行了展望。  

基于参数化运动合成的运动图构建及其应用

虚拟人运动的可控性和逼真性是虚拟现实应用中追求的重要目标,为了实现对虚拟人的灵活控制,合成出逼真的运动序列,提出了基于参数化运动合成的运动图方法.运动图的结点中存储了具有明确含义的控制参数,通过改变控制参数合成不同的运动片段,可以实现对虚拟人的灵活控制;提出了一种改进的运动融合方法,运动图的边将不同运动片段进行融合,有效的避免了脚步滑动和根关节朝向抖动的产生.根据用户对交互控制和路径轨迹的不同应用需求设计实验,实验结果表明,方法不仅具有较高的控制精度,而且合成的运动序列逼真自然.  

State of the Art in Example-Based Motion Synthesis for Virtual Characters in Interactive Applications

Animated virtual human characters are a common feature in interactive graphical applications, such as computer and video games, online virtual worlds and simulations. Due to dynamic nature of such applications, character animation must be responsive and controllable in addition to looking as realistic and natural as possible. Though procedural and physics-based animation provide a great amount of control over motion, they still look too unnatural to be of use in all but a few specific scenarios, which is why interactive applications nowadays still rely mainly on recorded and hand-crafted motion clips. The challenge faced by animation system designers is to dynamically synthesize new, controllable motion by concatenating short motion segments into sequences of different actions or by parametrically blending clips that correspond to different variants of the same logical action. In this article, we provide an overview of research in the field of example-based motion synthesis for interactive applications. We present methods for automated creation of supporting data structures for motion synthesis and describe how they can be employed at run-time to generate motion that accurately accomplishes tasks specified by the AI or human user.  

动作与音乐的节奏特征匹配模型

针对计算机动画制作中的自动编舞和配乐问题,提出一个基于节奏特征的动作-音乐匹配模型.首先分析出动作和音乐数据的节奏特征,然后使用动态时间规整算法度量动作与音乐片段的匹配程度,从而形成动作与音乐节奏匹配的计算模型.自动编舞或配乐流程包括预处理和实时匹配2个阶段.在预处理阶段,利用节奏特征匹配模型预先计算,获得数据库中所有潜在的动作和音乐片段组合的匹配程度,并得到一个动作-音乐映射图;在实时匹配阶段,首先通过图遍历的方法来搜索出与输入具有最佳节奏匹配的候选动作或者音乐,然后进一步对这些候选动作或者音乐数据的节奏特征点进行适当的优化调整,形成自动编舞或者配乐的结果.实验结果表明,该模型能够有效地指导用户编排出所期望的舞蹈动作或背景音乐.  

基于非线性流形学习的3维人体运动合成

为了实现3维人体运动的有效合成，提出了一种基于非线性流形学习的3维人体运动合成框架及算法,并可应用于方便、快捷、用户可控的3维人体运动合成。该合成算法框架先采用非线性流形降维方法将高维运动样本映射到低维流形上，同时求解其本征运动语义参数空间的表达，然后将用户在低维运动语义参数空间中交互生成的样本通过逆向映射重建得到具有新运动语义特征的3维运动序列。实验结果表明该方法不仅能够对运动物理参数（如特定关节的运动位置、物理运动特征）进行较为精确的控制，还可用于合成具有高层运动语义（运动风格）的新运动数据。与现有运动合成方法比较，该方法具有用户可控、交互性强等优点，能够应用于常见3维人体运动数据的高效生成。  

基于Kinect的高维人体动画实时合成研究

基于动作的视频交互游戏一直是游戏市场上非常受消费者青睐的主流游戏之一。研究利用Kinect从用户动作中获取低维控制信号,然后通过双级结构来重建高维度运动控制信号,以实现高质量人体动画的实时合成。其中第一级先通过构造一个邻居图缩小搜索空间,再通过K-D树加速搜索算法得到k个相似数据,最后基于主成分分析法来构建一个线性运动实时合成模型;第二级则是利用平滑参数对线性模型进行优化。实验结果表明,即使在场景受到严重干扰的情况下,该方法仍然可以重建出高质量的人体动画。  

基于高斯过程动态模型的人体节奏运动合成

提出一种新的基于高斯过程动态模型的节奏转移方法。该方法能够准确、有效地将现有运动中的节奏信息转移到新的运动中去，适用于各种不同类型的运动。首先，使用短时（short term）PCA计算源运动的节奏点，组合3种重要的运动特征求解目标运动的特征点；然后，使用动态规划算法来找到两者之间的最佳匹配，最大化减少计算时间及对目标运动的修改；最后，使用高斯过程动态模型对目标运动进行学习，并在隐空间进行节奏化插值，最终合成新的节奏化运动。