Fluid Simulation with Articulated Bodies

We present an algorithm for creating realistic animations of characters that are swimming through fluids. Our approach combines dynamic simulation with data-driven kinematic motions (motion capture data) to produce realistic animation in a fluid. The interaction of the articulated body with the fluid is performed by incorporating joint constraints with rigid animation and by extending a solid/fluid coupling method to handle articulated chains. Our solver takes as input the current state of the simulation and calculates the angular and linear accelerations of the connected bodies needed to match a particular motion sequence for the articulated body. These accelerations are used to estimate the forces and torques that are then applied to each joint. Based on this approach, we demonstrate simulated swimming results for a variety of different strokes, including crawl, backstroke, breaststroke, and butterfly. The ability to have articulated bodies interact with fluids also allows us to generate simulations of simple water creatures that are driven by simple controllers.     

Robot Head Motion Control with an Emphasis on Realism of Neck–Eye Coordination during Object Tracking

Important aspects of present-day humanoid robot research is to make such robots look realistic and human-like, both in appearance, as well as in motion and mannerism. In this paper, we focus our study on advanced control leading to realistic motion coordination for a humanoid’s robot neck and eyes while tracking an object. The motivating application for such controls is conversational robotics, in which a robot head “actor” should be able to detect and make eye contact with a human subject. Therefore, in such a scenario, the 3D position and orientation of an object of interest in space should be tracked by the redundant head–eye mechanism partly through its neck, and partly through its eyes. In this paper, we propose an optimization approach, combined with a real-time visual feedback to generate the realistic robot motion and robustify it. We also offer experimental results showing that the neck–eye motion obtained from the proposed algorithm is realistic comparing to the head–eye motion of humans.     

Using Dynamic Analysis for Realistic Animation of Articulated Bodies

A major problem in computer animation is creating motion that appears natural and realistic, particularly in such complex articulated bodies as humans and other animals. At present, truly lifelike motion is produced mainly by copying recorded images, a tedious and lengthy process that requires considerable external equipment. An alternative is the use of dynamic analysis to predict realistic motion. Using dynamic motion control, bodies are treated as masses acting under the influence of external and internal forces and torques. Dynamic control is advantageous because motion is naturally restricted to physically realizable patterns, and many types of motion can be predicted automatically. Use of dynamics is computationally expensive and specifying controlling forces and torques can be difficult. However, there is evidence that dynamics offers hope for more realistic, natural, and automatic motion control. Because such motion simulates real world conditions, an animation system using dynamic analysis is also a useful tool in such related fields as robotics and biomechanics.     

Model-based hand tracking using a hierarchical Bayesian filter

This paper sets out a tracking framework, which is applied to the recovery of three-dimensional hand motion from an image sequence. The method handles the issues of initialization, tracking, and recovery in a unified way. In a single input image with no prior information of the hand pose, the algorithm is equivalent to a hierarchical detection scheme, where unlikely pose candidates are rapidly discarded. In image sequences, a dynamic model is used to guide the search and approximate the optimal filtering equations. A dynamic model is given by transition probabilities between regions in parameter space and is learned from training data obtained by capturing articulated motion. The algorithm is evaluated on a number of image sequences, which include hand motion with self-occlusion in front of a cluttered background.     

Tracking and Motion Estimation of the Articulated Object: a Hierarchical Kalman Filter Approach

Real-time motion capture plays a very important role in various applications, such as 3D interface for virtual reality systems, digital puppetry, and real-time character animation. In this paper we challenge the problem of estimating and recognizing the motion of articulated objects using theoptical motion capturetechnique. In addition, we present an effective method to control the articulated human figure in realtime.The heart of this problem is the estimation of 3D motion and posture of an articulated, volumetric object using feature points from a sequence of multiple perspective views. Under some moderate assumptions such as smooth motion and known initial posture, we develop a model-based technique for the recovery of the 3D location and motion of a rigid object using a variation of Kalman filter. The posture of the 3D volumatric model is updated by the 2D image flow of the feature points for all views. Two novel concepts – the hierarchical Kalman filter (KHF) and the adaptive hierarchical structure (AHS) incorporating the kinematic properties of the articulated object – are proposed to extend our formulation for the rigid object to the articulated one. Our formulation also allows us to avoid two classic problems in 3D tracking: the multi-view correspondence problem, and the occlusion problem. By adding more cameras and placing them appropriately, our approach can deal with the motion of the object in a very wide area. Furthermore, multiple objects can be handled by managing multiple AHSs and processing multiple HKFs.We show the validity of our approach using the synthetic data acquired simultaneously from the multiple virtual camera in a virtual environment (VE) and real data derived from a moving light display with walking motion. The results confirm that the model-based algorithm works well on the tracking of multiple rigid objects.     

基于改进鲸鱼优化算法的多无人机围捕

无人机围捕是一项具有挑战性和现实意义的任务,为使无人机可以成功有效地围捕移动目标,提出一种基于动态预测围捕点和改进鲸鱼优化算法的多无人机围捕算法。在环境未知,目标运动轨迹未知的情况下,首先利用多项式拟合预测目标运动轨迹,通过动态预测步数得到预测点,在其周围设置围捕点,然后使用双向协商法为无人机合理分配各个目标点。针对鲸鱼优化算法容易陷入局部最优的缺点,提出基于自适应权重和改变螺旋线位置更新的方法,从而提升算法的开发能力和搜索能力。最终在不同实验环境下进行多次实验仿真,实验结果表明了所提出算法的有效性。     

Path planning directed motion control of virtual humans in complex environments

Natural motion synthesis of virtual humans have been studied extensively, however, motion control of virtual characters actively responding to complex dynamic environments is still a challenging task in computer animation. It is a labor and cost intensive animator-driven work to create realistic human motions of character animations in a dynamically varying environment in movies, television and video games. To solve this problem, in this paper we propose a novel approach of motion synthesis that applies the optimal path planning to direct motion synthesis for generating realistic character motions in response to complex dynamic environment. In our framework, SIPP (Safe Interval Path Planning) search is implemented to plan a globally optimal path in complex dynamic environments. Three types of control anchors to motion synthesis are for the first time defined and extracted on the obtained planning path, including turning anchors, height anchors and time anchors. Directed by these control anchors, highly interactive motions of virtual character are synthesized by motion field which produces a wide variety of natural motions and has high control agility to handle complex dynamic environments. Experimental results have proven that our framework is capable of synthesizing motions of virtual humans naturally adapted to the complex dynamic environments which guarantee both the optimal path and the realistic motion simultaneously.     

Forward dynamics based realistic animation of rigid bodies

This paper presents a new methodology for model and control of the motion of an (articulated) rigid body for the purposes of animation. The technique uses a parameter optimization method for forward dynamic simulation to obtain a good set of values for the control variables of the system. We model articulated rigid bodies using a moderate number of control nodes, and we linearly interpolate control values between adjacent pairs of these nodes. The interpolated control values are used to determine the forces/torques for the body actuators. We can control total motion duration time, and the control is more flexible than in any other dynamics based animation techniques. We employ a parameter optimization, (or nonlinear programming) method to find a good set of values for the control nodes. We extend this method by using a musculotendon skeletal model for the human body instead of the more commonly used robot model to provide more accurate human motion simulations. Skeletal and musculotendon dynamics enable us to do the human body animation more accurately than ever because the muscle force depends on the geometry of a human as well as on differential kinematic parameters. We show various levels of motion control for forward dynamics animation: ranging from piecewise linear forces/torques control for joints to muscle activation signal control for muscles to generate highly nonlinear forces/torques. This spectrum of control levels provides various nonlinear resulting motions to animators to allow them to achieve effective motion control and physically realistic motion simultaneously. Because our algorithms are heavily dependent on parameter optimization, and since the optimization technique may have difficulty finding a global optimum, we provide a modified optimization method along with various techniques to reduce the search space size. Our parameter optimization based forward dynamic animation and musculotendon dynamics based animation present the first use of such techniques in animation research to date.     

Interactive Construction and Animation of Layered Elastically Deformable Characters

An interactive system is described for creating and animating deformable 3D characters. By using a hybrid layered model of kinematic and physics-based components together with an immersive 3D direct manipulation interface, it is possible to quickly construct characters that deform naturally when animated and whose behavior can be controlled interactively using intuitive parameters. In this layered construction technique, called the elastic surface layer model, a simulated elastically deformable skin surface is wrapped around a kinematic articulated figure. Unlike previous layered models, the skin is free to slide along the underlying surface layers constrained by geometric constraints which push the surface out and spring forces which pull the surface in to the underlying layers. By tuning the parameters of the physics-based model, a variety of surface shapes and behaviors can be obtained such as more realistic-looking skin deformation at the joints, skin sliding over muscles, and dynamic effects such as squash-and-stretch and follow-through. Since the elastic model derives all of its input forces from the underlying articulated figure, the animator may specify all of the physical properties of the character once, during the initial character design process, after which a complete animation sequence can be created using a traditional skeleton animation technique. Character construction and animation are done using a 3D user interface based on two-handed manipulation registered with head-tracked stereo viewing. In our configuration, a six degree-of-freedom head-tracker and CrystalEyes shutter glasses are used to display stereo images on a workstation monitor that dynamically follow the user head motion. 3D virtual objects can be made to appear at a fixed location in physical space which the user may view from different angles by moving his head. To construct 3D animated characters, the user interacts with the simulated environment using both hands simultaneously: the left hand, controlling a Spaceball, is used for 3D navigation and object movement, while the right hand, holding a 3D mouse, is used to manipulate through a virtual tool metaphor the objects appearing in front of the screen. Hand-eye coordination is made possible by registering virtual space to physical space, allowing a variety of complex 3D tasks necessary for constructing 3D animated characters to be performed more easily and more rapidly than is possible using traditional interactive techniques.     

Dynamic motion planning of overarm throw for a biped human multibody system

We propose a motion planning formulation of overarm throw for a 55-degree-of-freedom biped human multibody system. The unique characteristics of the throwing task—highly redundant, highly nonlinear, and highly dynamic—make the throwing motion simulation challenging in the literature and are addressed within the framework of multibody dynamics and optimization. To generate physically feasible throwing motions in a fully predictive method without input reference from motion capture or animation, rigorous dynamics modeling, such as dynamic balance based on Zero-Moment Point (ZMP) and ground reaction loads, is associated with the constraints. Given the target location and the object mass, the algorithm outputs the motion, required actuator torques, release conditions, and projectile and flight time of the object. Realistic human-like motions of throwing are generated for different input parameters, which demonstrate valid cause–effect relations in terms of both kinematic and kinetic outputs.     

Generating human-like soccer primitives from human data

Recently, interest in analysis and generation of human and human-like motion has increased in various areas. In robotics, in order to operate a humanoid robot, it is necessary to generate motions that have strictly dynamic consistency. Furthermore, human-like motion for robots will bring advantages such as energy optimization.This paper presents a mechanism to generate two human-like motions, walking and kicking, for a biped robot using a simple model based on observation and analysis of human motion. Our ultimate goal is to establish a design principle of a controller in order to achieve natural human-like motions. The approach presented here rests on the principle that in most biological motor learning scenarios some form of optimization with respect to a physical criterion is taking place. In a similar way, the equations of motion for the humanoid robot systems are formulated in such a way that the resulting optimization problems can be solved reliably and efficiently.The simulation results show that faster and more accurate searching can be achieved to generate an efficient human-like gait. Comparison is made with methods that do not include observation of human gait. The gait has been successfully used to control Robo-Erectus, a soccer-playing humanoid robot, which is one of the foremost leading soccer-playing humanoid robots in the RoboCup Humanoid League.     

Mechatronic design of the Twente humanoid head

This paper describes the mechatronic design of the Twente humanoid head, which has been realized in the purpose of having a research platform for human-machine interaction. The design features a fast, four degree of freedom neck, with long range of motion, and a vision system with three degrees of freedom, mimicking the eyes. To achieve fast target tracking, two degrees of freedom in the neck are combined in a differential drive, resulting in a low moving mass and the possibility to use powerful actuators. The performance of the neck has been optimized by minimizing backlash in the mechanisms, and using gravity compensation. The vision system is based on a saliency algorithm that uses the camera images to determine where the humanoid head should look at, i.e. the focus of attention computed according to biological studies. The motion control algorithm receives, as input, the output of the vision algorithm and controls the humanoid head to focus on and follow the target point. The control architecture exploits the redundancy of the system to show human-like motions while looking at a target. The head has a translucent plastic cover, onto which an internal LED system projects the mouth and the eyebrows, realizing human-like facial expressions.     

Genetic programming for articulated figure motion

We present an approach to articulated figure motion in which motion tasks are defined in terms of goals and ratings. The agents are dynamically-controlled robots whose behaviour is determined by robotic controller programs. The controller programs for the robots are evaluated at each time step to yield torque values which drive the dynamic simulation of the motion. We use the AI technique of genetic programming (GP) to automatically derive control programs for the agents which achieve the goals. This type of motion specification is an alternative to key framing which allows a highly automated, learning-based approach to generation of motion. This method of motion control is very general (it can be applied to any type of motion), yet it allows for specifications of the types of specific motion which are desired for a high quality animation. We show that complex, specific, physically plausible and aesthetically appealing motion can be generated using these methods.     

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Processing实现多智能体环形围捕仿真

在以往多智能体编队研究的仿真分析中,相关物理量和运动轨迹常采用静态曲线描述,较少关注系统运动过程的实时动态刻画。针对上述问题,提出了一种基于Processing的动态仿真方法,首先,利用三阶贝塞尔曲线构造了一种新型软体仿生鱼模型。其次,综合虚拟结构法和人工势场法设计了能解决个体避碰问题的多智能体环形围捕控制律。最后,应用仿生鱼模型对多智能体环形围捕控制算法进行动态仿真。仿真结果表明,新型仿真方法直观增强了智能体位姿信息的动态可视化,验证了二阶积分器多智能体系统环形围捕算法的可行性。     

Robust Articulated‐ICP for Real‐Time Hand Tracking

We present a robust method for capturing articulated hand motions in realtime using a single depth camera. Our system is based on a realtime registration process that accurately reconstructs hand poses by fitting a 3D articulated hand model to depth images. We register the hand model using depth, silhouette, and temporal information. To effectively map low‐quality depth maps to realistic hand poses, we regularize the registration with kinematic and temporal priors, as well as a data‐driven prior built from a database of realistic hand poses. We present a principled way of integrating such priors into our registration optimization to enable robust tracking without severely restricting the freedom of motion. A core technical contribution is a new method for computing tracking correspondences that directly models occlusions typical of single‐camera setups. To ensure reproducibility of our results and facilitate future research, we fully disclose the source code of our implementation.     

3D Articulated Models and Multiview Tracking with Physical Forces

We present a method for automatically estimating the motion of an articulated object filmed by two or more fixed cameras. We focus our work on the case where the quality of the images is poor, and where only an approximation of a geometric model of the tracked object is available. Our technique uses physical forces applied to each rigid part of a kinematic 3D model of the object we are tracking. These forces guide the minimization of the differences between the pose of the 3D model and the pose of the real object in the video images. We use a fast recursive algorithm to solve the dynamical equations of motion of any 3D articulated model. We explain the key parts of our algorithms: how relevant information is extracted from the images, how the forces are created, and how the dynamical equations of motion are solved. A study of what kind of information should be extracted in the images and of when our algorithms fail is also presented. Finally we present some results about the tracking of a person. We also show the application of our method to the tracking of a hand in sequences of images, showing that the kind of information to extract from the images depends on their quality and of the configuration of the cameras.     

Simulation‐Ready Hair Capture

Physical simulation has long been the approach of choice for generating realistic hair animations in CG. A constant drawback of simulation, however, is the necessity to manually set the physical parameters of the simulation model in order to get the desired dynamic behavior. To alleviate this, researchers have begun to explore methods for reconstructing hair from the real world and even to estimate the corresponding simulation parameters through the process of inversion. So far, however, these methods have had limited applicability, because dynamic hair capture can only be played back without the ability to edit, and solving for simulation parameters can only be accomplished for static hairstyles, ignoring the dynamic behavior. We present the first method for capturing dynamic hair and automatically determining the physical properties for simulating the observed hairstyle in motion. Since our dynamic inversion is agnostic to the simulation model, the proposed method applies to virtually any hair simulation technique, which we demonstrate using two state‐of‐the‐art hair simulation models. The output of our method is a fully simulation‐ready hairstyle, consisting of both the static hair geometry as well as its physical properties. The hairstyle can be easily edited by adding additional external forces, changing the head motion, or re‐simulating in completely different environments, all while remaining faithful to the captured hairstyle.     

Ad hoc网络中TDMA分布式动态时隙分配算法

基于时分多址(TDMA)的时隙分配算法能够提供很好的无线资源利用率,特别是在高负载的环境下.提出了一种适于Ad hoc基于TDMA的无冲突动态分布式时隙分配算法,通过动态改变帧长来控制未用时隙的过量增长,提高了系统吞吐量.该算法通过设置帧长为时隙2的次方,使其在不同帧长的节点中无冲突地包传输.节点间的同步采用本地同步方式.仿真结果表明该算法与IEEE 802.11相比提高了系统吞吐量并降低了端-端延迟.