Key Probe: a technique for animation keyframe extraction

We present a novel constraint-based keyframe extraction technique, Key Probe. Based on animator-specified constraints, the method converts a skeleton-based motion or animated mesh to a keyframe-based representation. In contrast to previous curve simplification or clustering methods, we cast the keyframe extraction problem as a constrained matrix factorization problem and solve the problem based on the least-squares optimization technique. The extracted keyframes have two uses: they could be used for browsing or they may be blended to reconstruct all other frames of an animation. Our approach is general and suitable for both rigid-body and soft-body animations. Experiments on various types of animation examples show that the proposed method produces remarkable results in terms of quality and compression ratio. Empirical tests also show that our algorithm consistently offers better efficiency than those by principal component analysis (PCA) and independent component analysis (ICA).     

Multiscale motion saliency for keyframe extraction from motion capture sequences

Motion capture is an increasingly popular animation technique; however data acquired by motion capture can become substantial. This makes it difficult to use motion capture data in a number of applications, such as motion editing, motion understanding, automatic motion summarization, motion thumbnail generation, or motion database search and retrieval. To overcome this limitation, we propose an automatic approach to extract keyframes from a motion capture sequence. We treat the input sequence as motion curves, and obtain the most salient parts of these curves using a new proposed metric, called 'motion saliency'. We select the curves to be analysed by a dimension reduction technique, Principal Component Analysis (PCA). We then apply frame reduction techniques to extract the most important frames as keyframes of the motion. With this approach, around 8% of the frames are selected to be keyframes for motion capture sequences. Copyright © 2010 John Wiley & Sons, Ltd.     

Optimized keyframe extraction for 3D character animations

In this paper, we propose a new method to automatically extract keyframes from animation sequences. Our method can be applied equally to both skeletal and mesh animations. It uses animation saliency computed on the original data to help select the group of keyframes that can reconstruct the input animation with less perception error. For computational efficiency, we perform nonlinear dimension reduction using locally linear embedding and then carry out the optimal search in much lower‐dimensional space. With this approach, reconstruction of the animation from the extracted keyframes shows much better results as compared with earlier approaches. Copyright © 2012 John Wiley & Sons, Ltd.     

A performance-based technique for timing keyframe animations

From our experience observing novice animators, it is clear that setting keyframe spatial values is straightforward while specifying the keyframe timing is difficult and often time consuming. This paper presents a modified approach to the keyframing paradigm, performance timing, that lets the user focus on the timing of keyframes separately from the spatial values. In performance timing, the user “acts-out” the timing information using a simple 2D input device such as a mouse or pen-tablet. The user’s input is analyzed and features of the user’s input are mapped to the spatial features of the keyframed motion. The keyframes are then distributed in time according to the timing of the user’s input path. We demonstrate the approach on several scenes and discuss the situations in which it is most and least effective. We present the results of a user study of over 20 subjects in which we compare accuracy using performance timing to accuracy using a standard animation package for specifying keyframe timing.     

Non-linear Rough 2D Animation using Transient Embeddings

Traditional 2D animation requires time and dedication since tens of thousands of frames need to be drawn by hand for a typical production. Many computer-assisted methods have been proposed to automatize the generation of inbetween frames from a set of clean line drawings, but they are all limited by a rigid workflow and a lack of artistic controls, which is in the most part due to the one-to-one stroke matching and interpolation problems they attempt to solve. In this work, we take a novel view on those problems by focusing on an earlier phase of the animation process that uses rough drawings (i.e., sketches). Our key idea is to recast the matching and interpolation problems so that they apply to transient embeddings, which are groups of strokes that only exist for a few keyframes. A transient embedding carries strokes between keyframes both forward and backward in time through a sequence of transformed lattices. Forward and backward strokes are then cross-faded using their thickness to yield rough inbetweens. With our approach, complex topological changes may be introduced while preserving visual motion continuity. As demonstrated on state-of-the-art 2D animation exercises, our system provides unprecedented artistic control through the non-linear exploration of movements and dynamics in real-time.     

Optimization‐based group performance deducing

Large‐scale group performance animation has been an important research topic because of its diverse range of applications including virtual rehearsal and film production. Animating hundreds of virtual actors as what the director wishes is a tough task. In this paper, we address this challenge by introducing an optimization method that generates large‐scale group performance by deducing a small‐scale one with fewer actors. We introduced group motion bigraph technique and transformed the motion‐deducing problem into a constrained optimization problem. A solving process is then presented to automatically obtain the motion of the large group with velocity constraints. Moreover, an interactive system of constructing the group motion bigraph has been implemented, which provides flexible edit and control on deducing group motion. The animation results show that our method is competent for deducing large‐scale group performance from only several motion clips performed by small groups. Copyright © 2013 John Wiley & Sons, Ltd.     

基于混合遗传算法的人体运动捕获数据关键帧提取

为实现基于最佳关键帧集合的人体运动的紧致表示,提出一种遗传算法与单纯形法结合的人体运动捕获数据关键帧提取方法。以重构误差最小化和压缩率最优为目标,定义适应度函数,度量重构运动与原始运动之间的重构误差,通过关节位置和关节速率加权计算,并考虑数据的运动特性。利用背景知识对初始种群的个体进行优化,保证进化的良好基础和种群的多样性。将遗传算法和局部搜索技术结合,提高算法运行效率和求解质量。实验结果表明,该方法能够高效地从运动捕获数据中提取出最优的关键帧集合,较好地满足运动数据的紧致表示,且能高质量重构其它帧。     

Accelerating Liquid Simulation With an Improved Data-Driven Method

In physics-based liquid simulation for graphics applications, pressure projection consumes a significant amount of computational time and is frequently the bottleneck of the computational efficiency. How to rapidly apply the pressure projection and at the same time how to accurately capture the liquid geometry are always among the most popular topics in the current research trend in liquid simulations. In this paper, we incorporate an artificial neural network into the simulation pipeline for handling the tricky projection step for liquid animation. Compared with the previous neural-network-based works for gas flows, this paper advocates new advances in the composition of representative features as well as the loss functions in order to facilitate fluid simulation with free-surface boundary. Specifically, we choose both the velocity and the level-set function as the additional representation of the fluid states, which allows not only the motion but also the boundary position to be considered in the neural network solver. Meanwhile, we use the divergence error in the loss function to further emulate the lifelike behaviours of liquid. With these arrangements, our method could greatly accelerate the pressure projection step in liquid simulation, while maintaining fairly convincing visual results. Additionally, our neutral network performs well when being applied to new scene synthesis even with varied boundaries or scales.     

Multi-camera tele-immersion system with real-time model driven data compression

Vision-based full-body 3D reconstruction for tele-immersive applications generates large amount of data points, which have to be sent through the network in real time. In this paper, we introduce a skeleton-based compression method using motion estimation where kinematic parameters of the human body are extracted from the point cloud data in each frame. First we address the issues regarding the data capturing and transfer to a remote site for the tele-immersive collaboration. We compare the results of the existing compression methods and the proposed skeleton-based compression technique. We examine the robustness and efficiency of the algorithm through experimental results with our multi-camera tele-immersion system. The proposed skeleton-based method provides high and flexible compression ratios from 50:1 to 5000:1 with reasonable reconstruction quality (peak signal-to-noise ratio from 28 to 31 dB) while preserving real-time (10+ fps) processing.     

四元数样条插值的人体运动数据重构

为了得到平滑的人体动画,提出一种基于四元数的样条插值算法,利用提取的关键帧实现人体运动序列的有效重构。为减少重构误差、加快收敛速度,将已知关键帧集合作为初始条件,通过迭代算法求出样条曲线的控制点集合。利用样条曲线控制点计算贝塞尔曲线控制点,构造贝塞尔样条曲线段,将各段贝塞尔样条曲线段组合,构造一条基于四元数的样条曲线。根据德卡斯特里奥（de Casteljau）算法插值重构人体运动。实验结果表明,该算法在保证执行效率的同时,可得到光滑的插值结果,实现满足视觉要求的人体运动重构。     

Video-based personalized traffic learning

We present a video-based approach to learn the specific driving characteristics of drivers in the video for advanced traffic control. Each vehicle’s specific driving characteristics are calculated with an offline learning process. Given each vehicle’s initial status and the personalized parameters as input, our approach can vividly reproduce the traffic flow in the sample video with a high accuracy. The learned characteristics can also be applied to any agent-based traffic simulation systems. We then introduce a new traffic animation method that attempts to animate each vehicle with its real driving habits and show its adaptation to the surrounding traffic situation. Our results are compared to existing traffic animation methods to demonstrate the effectiveness of our presented approach.     

Real‐time generation of smoothed‐particle hydrodynamics‐based special effects in character animation

In the previous works, the real‐time fluid‐character animation could hardly be achieved because of the intensive processing demand on the character's movement and fluid simulation. This paper presents an effective approach to the real‐time generation of the fluid flow driven by the motion of a character in full 3D space, based on smoothed‐particle hydrodynamics method. The novel method of conducting and constraining the fluid particles by the geometric properties of the character motion trajectory is introduced. Furthermore, the optimized algorithms of particle searching and rendering are proposed, by taking advantage of the graphics processing unit parallelization. Consequently, both simulation and rendering of the 3D liquid effects with realistic character interactions can be implemented by our framework and performed in real‐time on a conventional PC. Copyright © 2013 John Wiley & Sons, Ltd.     

Enriching Facial Blendshape Rigs with Physical Simulation

Oftentimes facial animation is created separately from overall body motion. Since convincing facial animation is challenging enough in itself, artists tend to create and edit the face motion in isolation. Or if the face animation is derived from motion capture, this is typically performed in a mo‐cap booth while sitting relatively still. In either case, recombining the isolated face animation with body and head motion is non‐trivial and often results in an uncanny result if the body dynamics are not properly reflected on the face (e.g. the bouncing of facial tissue when running). We tackle this problem by introducing a simple and intuitive system that allows to add physics to facial blendshape animation. Unlike previous methods that try to add physics to face rigs, our method preserves the original facial animation as closely as possible. To this end, we present a novel simulation framework that uses the original animation as per‐frame rest‐poses without adding spurious forces. As a result, in the absence of any external forces or rigid head motion, the facial performance will exactly match the artist‐created blendshape animation. In addition we propose the concept of blendmaterials to give artists an intuitive means to account for changing material properties due to muscle activation. This system allows to automatically combine facial animation and head motion such that they are consistent, while preserving the original animation as closely as possible. The system is easy to use and readily integrates with existing animation pipelines.     

Detailed traffic animation for urban road networks

We present a new agent-based system for detailed traffic animation on urban arterial networks with diverse junctions like signalized crossing, merging and weaving areas. To control the motion of traffic for visualization and animation purposes, we utilize the popular follow-the-leader method to simulate various vehicle types and intelligent driving styles. We also introduce a continuous lane-changing model to imitate the vehicle’s decision-making process and dynamic interactions with neighboring vehicles. By applying our approach in several typical urban traffic scenarios, we demonstrate that our system can well visualize vehicles’ behaviors in a realistic manner on complex road networks and generate immersive traffic flow animations with smooth accelerating strategies and flexible lane changes.     

模仿学习:一种新人工生命动画方法

提出一种新的人工生命动画方法—模仿学习. 模仿是一种非常有效的掌握运动技能的学习方式. 一项运动技能为无数个相关运动序列的集合. 通过模仿代表性运动序列,将蕴含的局部运动技能泛化,可获得完整的运动技能. 模仿学习以运动相似度匹配和简单--复杂行为方法论为核心,并以进化计算为优化方法. 模仿学习降低进化计算对传统评价函数的依赖,减少评价函数设计时间,提高优化复杂目标的能力,因此提高了制作效率. 基于PhysX仿真平台,本文以人工猫的着陆行为验证了本文方法的有效性,并取得了良好的效果.     

Wake Synthesis For Shallow Water Equation

In fluid animation, wake is one of the most important phenomena usually seen when an object is moving relative to the flow. However, in current shallow water simulation for interactive applications, this effect is greatly smeared out. In this paper, we present a method to efficiently synthesize these wakes. We adopt a generalized SPH method for shallow water simulation and two way solid fluid coupling. In addition, a 2D discrete vortex method is used to capture the detailed wake motions behind an obstacle, enriching the motion of SWE simulation. Our method is highly efficient since only 2D simulation is required. Moreover, by using a physically inspired procedural approach for particle seeding, DVM particles are only created in the wake region. Therefore, very few particles are required while still generating realistic wake patterns. When coupled with SWE, we show that these patterns can be seen using our method with marginal overhead.     

Approach to Nonrigid Body Metamorphosis UsingGeneralized Morph-translation

An approach of morphing by decomposing two objects into sets of individual convex sub objects respectively and contructing the mapping between two sets is presented The Minkvski addition of two convex sub-objects according to their mapping ralationship is calculated, then all the Minkovski additions are combined to obtain the final result. Nonrigid body motion can be divided into nonrigid body metamorphosis and rigid body rotation A novel method for describing nonrigid body motion based on generalized morph-translation is proposed. This method can solve the metamorphosis problem of two non-homotopic objects. Experiments show tha this method can generate natural, high quality metamorphosis results with simple computation. This method can also be used in interpolation between two keyframes in 2D and 3D computer animation automatically.     

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.