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.     

计算机角色动画制作中的可视媒体融合综述

近年来,计算机角色动画在虚拟现实、计算机游戏、动画生成、影视特技及运动仿真等多个领域得到广泛深入的应用,同时在数据上涉及到图像、视频、运动捕获数据、三维模型等可视媒体,已经成为一个多种可视媒体融合的研究领域．在计算机角色动画制作中,传统的仅仅利用单一可视媒体进行模型制作和动画生成的方法正在被基于多种可视媒体融合的方法所取代．文中从可视媒体融合的角度对现有的计算机角色动画研究进行综述,并依据可视媒体融合框架进行归纳和分类．     

基于Hermite样条曲线的关键帧插值角色动画

针对角色关键帧动画中的运动平滑性问题,以基于运动捕获数据的角色动画为基础,提出以Hermite样条曲线为基本算法结合四元数球面线性插值算法的方法,对虚拟人体骨骼运动的动画关键帧进行插值,实现虚拟人体角色的动作平滑过渡。经实验证明,本方法实现简单高效,获得的关键帧动画平滑流畅,可广泛用于人体类角色动画的研究。     

Natural preparation behavior synthesis

Humans adjust their movements in advance to prepare for the forthcoming action, resulting in efficient and smooth transitions. However, traditional computer animation approaches such as motion graphs simply concatenate a series of actions without taking into account the following one. In this paper, we propose a new method to produce preparation behaviors using reinforcement learning. As an offline process, the system learns the optimal way to approach a target and to prepare for interaction. A scalar value called the level of preparation is introduced, which represents the degree of transition from the initial action to the interacting action. To synthesize the movements of preparation, we propose a customized motion blending scheme based on the level of preparation, which is followed by an optimization framework that adjusts the posture to keep the balance. During runtime, the trained controller drives the character to move to a target with the appropriate level of preparation, resulting in a humanlike behavior. We create scenes in which the character has to move in a complex environment and to interact with objects, such as crawling under and jumping over obstacles while walking. The method is useful not only for computer animation but also for real‐time applications such as computer games, in which the characters need to accomplish a series of tasks in a given environment. Copyright © 2013 John Wiley & Sons, Ltd.     

HandPuppet3D: Motion capture and analysis for character animation

Motion capture is a technique of digitally recording the movements of real entities, usually humans. It was originally developed as an analysis tool in biomechanics research, but has grown increasingly important as a source of motion data for computer animation. In this context it has been widely used for both cinema and video games. Hand motion capture and tracking in particular has received a lot of attention because of its critical role in the design of new Human Computer Interaction methods and gesture analysis. One of the main difficulties is the capture of human hand motion. This paper gives an overview of ongoing research “HandPuppet3D” being carried out in collaboration with an animation studio to employ computer vision techniques to develop a prototype desktop system and associated animation process that will allow an animator to control 3D character animation through the use of hand gestures. The eventual goal of the project is to support existing practice by providing a softer, more intuitive, user interface for the animator that improves the productivity of the animation workflow and the quality of the resulting animations. To help achieve this goal the focus has been placed on developing a prototype camera based desktop gesture capture system to capture hand gestures and interpret them in order to generate and control the animation of 3D character models. This will allow an animator to control 3D character animation through the capture and interpretation of hand gestures. Methods will be discussed for motion tracking and capture in 3D animation and in particular that of hand motion tracking and capture. HandPuppet3D aims to enable gesture capture with interpretation of the captured gestures and control of the target 3D animation software. This involves development and testing of a motion analysis system built from algorithms recently developed. We review current software and research methods available in this area and describe our current work.     

A Dynamic Motion Control Technique for Human-like Articulated Figures

This paper presents a dynamic motion control technique for human-like articulated figures in a physically based character animation system. This method controls a figure such that the figure tracks input motion specified by a user. When environmental physical input such as an external force or a collision impulse are applied to the figure, this method generates dynamically changing motion in response to the physical input. We have introduced comfort and balance control to compute the angular acceleration of the figure's joints. Our algorithm controls the several parts of a human-like articulated figure separetely through the minimum number of degrees-of-freedom. Using this approach, our algorithm simulates realistic human motions at efficient computational cost. Unlike existing dynamic simulation systems, our method assumes that input motion is already realistic, and is aimed at dynamically changing the input motion in real-time only when unexpected physical input is applied to the figure. As such, our method works efficiently in the framework of current computer games.     

Synthesizing Character Animation with Smoothly Decomposed Motion Layers

The processing of captured motion is an essential task for undertaking the synthesis of high-quality character animation. The motion decomposition techniques investigated in prior work extract meaningful motion primitives that help to facilitate this process. Carefully selected motion primitives can play a major role in various motion-synthesis tasks, such as interpolation, blending, warping, editing or the generation of new motions. Unfortunately, for a complex character motion, finding generic motion primitives by decomposition is an intractable problem due to the compound nature of the behaviours of such characters. Additionally, decomposed motion primitives tend to be too limited for the chosen model to cover a broad range of motion-synthesis tasks. To address these challenges, we propose a generative motion decomposition framework in which the decomposed motion primitives are applicable to a wide range of motion-synthesis tasks. Technically, the input motion is smoothly decomposed into three motion layers. These are base-level motion, a layer with controllable motion displacements and a layer with high-frequency residuals. The final motion can easily be synthesized simply by changing a single user parameter that is linked to the layer of controllable motion displacements or by imposing suitable temporal correspondences to the decomposition framework. Our experiments show that this decomposition provides a great deal of flexibility in several motion synthesis scenarios: denoising, style modulation, upsampling and time warping.     

Manifold-valued Thin-Plate Splines with Applications in Computer Graphics

We present a generalization of thin‐plate splines for interpolation and approximation of manifold‐valued data, and demonstrate its usefulness in computer graphics with several applications from different fields. The cornerstone of our theoretical framework is an energy functional for mappings between two Riemannian manifolds which is independent of parametrization and respects the geometry of both manifolds. If the manifolds are Euclidean, the energy functional reduces to the classical thin‐plate spline energy. We show how the resulting optimization problems can be solved efficiently in many cases. Our example applications range from orientation interpolation and motion planning in animation over geometric modelling tasks to color interpolation.     

基于马尔可夫决策过程的群体动画运动轨迹生成

近些年来,群体动画在机器人学、电影、游戏等领域得到了广泛的研究和应用,但传统的群体动画技术均涉及复杂的运动规划或碰撞避免操作,计算效率较低.本文提出了一种基于马尔可夫决策过程（MDPs）的群体动画运动轨迹生成算法,该算法无需碰撞检测即可生成各智能体的无碰撞运动轨迹.同时本文还提出了一种改进的值迭代算法用于求解马尔可夫决策过程的状态-值,利用该算法在栅格环境中进行实验,结果表明该算法的计算效率明显高于使用欧氏距离作为启发式的值迭代算法和Dijkstra算法.利用本文提出的运动轨迹生成算法在三维（3D）动画场景中进行群体动画仿真实验,结果表明该算法可实现群体无碰撞地朝向目标运动,并具有多样性.     

基于隐曲线的速度插值算法研究与实现

本文主要研究了隐曲线在行为动画中的应用,提出并进一步研究了隐曲线在行为动画中的任务指定作用.提出了基于隐曲线的速度插值算法.隐曲线用于指定行为动画的运动路径,速度曲线用于描述运动的时间分布.基于角色的运动路径和运动速度,速度插值技术生成与速度映射的插值位置点,进而通过逆向运动学技术,可以求解得到动画角色的骨架序列.同时对本文提出的隐曲线速度插值算法进行了实现和分析.通过设置不同的运动路径和速度曲线,速度插值技术可直观、快速地满足用户的具体要求创建不同的运动序列,且运动复用性强.     

Communication-aware heuristics for run-time task mapping on NoC-based MPSoC platforms

Efficient run-time mapping of tasks onto Multiprocessor System-on-Chip (MPSoC) is very challenging especially when new tasks of other applications are also required to be supported at run-time. In this paper, we present a number of communication-aware run-time mapping heuristics for the efficient mapping of multiple applications onto an MPSoC platform in which more than one task can be supported by each processing element (PE). The proposed mapping heuristics examine the available resources prior to recommending the adjacent communicating tasks on to the same PE. In addition, the proposed heuristics give priority to the tasks of an application in close proximity so as to further minimize the communication overhead. Our investigations show that the proposed heuristics are capable of alleviating Network-on-Chip (NoC) congestion bottlenecks when compared to existing alternatives. We map tasks of applications onto an 8 × 8 NoC-based MPSoC to show that our mapping heuristics consistently leads to reduction in the total execution time, energy consumption, average channel load and latency. In particular, we show that energy savings can be up to 44% and average channel load is improved by 10% for some cases.     

Handling mixed sets of tasks in combined offline and online scheduled real-time systems

Many industrial applications with real-time demands are composed of mixed sets of tasks with a variety of requirements. These can be in the form of standard timing constraints, such as period and deadline, or complex, e.g., to express application specific or nontemporal constraints, reliability, performance, etc. As many algorithms focus on specific sets of task types and constraints only, system design has to focus on those supported by a particular algorithm, at the expense of the rest. In this paper, we present a method to deal with a combination of mixed sets of tasks and constraints: periodic tasks with complex and simple constraints, soft and firm aperiodic, and sporadic tasks. We propose the use of an offline scheduler to manage complex timing and resource constraints of periodic tasks and transform these into a simple EDF model with start-times and deadlines. At run-time, the execution of the offline scheduled tasks is flexibly shifted in order to allow for feasible inclusion of dynamically arriving sporadic and aperiodic tasks. Sporadic tasks are guaranteed offline based on their worst-case activation frequencies. At run-time, this pessimism is reduced by the online algorithm which uses the exact knowledge about sporadic arrivals to reclaim resources and improve response times and acceptance of firm aperiodic tasks.     

基于Kinect的人体姿态估计优化和动画生成

为了生成更准确流畅的虚拟人动画,采用Kinect设备捕获三维人体姿态数据的同时,使用单目人体三维姿态估计算法对Kinect的彩色信息进行骨骼点数据推理,从而实时优化人体姿态估计效果,并驱动虚拟人物模型生成动画。首先,提出了一种时空优化的骨骼点数据处理方法,以提高单目估计人体三维姿态的稳定性;其次,提出了一种Kinect和遮挡鲁棒姿势图（ORPM）算法融合的人体姿态估计方法来解决Kinect的遮挡问题;最后,研制了基于四元数向量插值和逆向运动学约束的虚拟人动画系统,其能够进行运动仿真和实时动画生成。与仅利用Kinect捕获人体运动来生成动画的方法相比,所提方法的人体姿态估计数据鲁棒性更强,具备一定的防遮挡能力,而与基于ORPM算法的动画生成方法相比,所提方法生成的动画在帧率上提高了两倍,效果更真实流畅。     

An efficient control over human running animation with extension of planar hopper model

The most important goal of character animation is to efficiently control the motions of a character. Until now, many techniques have been proposed for human gait animation. Some techniques have been created to control the emotions in gaits such as ‘tired walking’ and ‘brisk walking’ by using parameter interpolation or motion data mapping. Since it is very difficult to automate the control over the emotion of a motion, the emotions of a character model have been generated by creative animators. This paper proposes a human running model based on a one‐legged planar hopper with a self‐balancing mechanism. The proposed technique exploits genetic programming to optimize movement and can be easily adapted to various character models. We extend the energy minimization technique to generate various motions in accordance with emotional specifications. Copyright © 1999 John Wiley & Sons, Ltd.     

Human motion capture data compression by model-based indexing: a power aware approach

Human motion capture (MoCap) data can be used for animation of virtual human-like characters in distributed virtual reality applications and networked games. MoCap data compressed using the standard MPEG-4 encoding pipeline comprising of predictive encoding (and/or DCT decorrelation), quantization, and arithmetic/Huffman encoding, entails significant power consumption for the purpose of decompression. In this paper, we propose a novel algorithm for compression of MoCap data, which is based on smart indexing of the MoCap data by exploiting structural information derived from the skeletal virtual human model. The indexing algorithm can be fine-controlled using three predefined quality control parameters (QCPs). We demonstrate how an efficient combination of the three QCPs results in a lower network bandwidth requirement and reduced power consumption for data decompression at the client end when compared to standard MPEG-4 compression. Since the proposed algorithm exploits structural information derived from the skeletal virtual human model, it is observed to result in virtual human animation of visually acceptable quality upon decompression     

Interpolation synthesis of articulated figure motion

Most conventional media depend on engaging and appealing characters. Empty spaces and buildings would not fare well as television or movie programming, yet virtual reality usually offers up such spaces. The problem lies in the difficulty of creating computer generated characters that display real time, engaging interaction and realistic motion. Articulated figure motion for real time computer graphics offers one solution to this problem. A common approach stores a set of motions and lets you choose one particular motion at a time. The article describes a process that greatly expands the range of possible motions. Mixing motions selected from a database lets you create a new motion to exact specifications. The synthesized motion retains the original motions' subtle qualities, such as the realism of motion capture or the expressive, exaggerated qualities of artistic animation. Our method provides a new way to achieve inverse kinematics capability-for example, placing the hands or feet of an articulated figure in specific positions. It proves useful for both real time graphics and prerendered animation production. The method, called interpolation synthesis, is based on motion capture data and it provides real time character motion for interactive entertainment or avatars in virtual worlds     

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。     

Formal approach to agent-based dynamic reconfiguration in Networks-On-Chip

A Network-On-Chip (NoC) platform is an emerging topology for large-scale applications. It provides a required number of resources for critical and excessive computations. However, the computations may be interrupted by faults occurring at run-time. Hence, reliability of computations as well as efficient resource management at run-time are crucial for such many-core NoC systems. To achieve this, we utilize an agent-based management system where agents are organized in a three-level hierarchy. We propose to incorporate reallocation and reconfiguration procedures into agents hierarchy such that fault-tolerance mechanisms can be executed at run-time. Task reallocation enables local reconfiguration of a core allowing it to be eventually reused in order to restore the original performance of communication and computations. The contributions of this paper are: (i) an algorithm for initial application mapping with spare cores, (ii) a multi-objective algorithm for efficient utilization of spare cores at run-time in order to enhance fault-tolerance while maintaining efficiency of communication and computations at an adequate level, (iii) an algorithm integrating the local reconfiguration procedure and (iv) formal modeling and verification of the dynamic agent-based NoC management architecture incorporating these algorithms within the Event-B framework.     

Data‐Driven Crowd Motion Control With Multi‐Touch Gestures

Controlling a crowd using multi‐touch devices appeals to the computer games and animation industries, as such devices provide a high‐dimensional control signal that can effectively define the crowd formation and movement. However, existing works relying on pre‐defined control schemes require the users to learn a scheme that may not be intuitive. We propose a data‐driven gesture‐based crowd control system, in which the control scheme is learned from example gestures provided by different users. In particular, we build a database with pairwise samples of gestures and crowd motions. To effectively generalize the gesture style of different users, such as the use of different numbers of fingers, we propose a set of gesture features for representing a set of hand gesture trajectories. Similarly, to represent crowd motion trajectories of different numbers of characters over time, we propose a set of crowd motion features that are extracted from a Gaussian mixture model. Given a run‐time gesture, our system extracts the K nearest gestures from the database and interpolates the corresponding crowd motions in order to generate the run‐time control. Our system is accurate and efficient, making it suitable for real‐time applications such as real‐time strategy games and interactive animation controls.