视频驱动人脸动画合成

描述了一种实时的视频驱动的人脸动画合成系统.通过该系统,用户只要在摄像头前面表演各种脸部动作,就可以控制虚拟人脸的表情.首先,建立一个基于肌肉的三维人脸模型,并使用肌肉激励参数控制人脸形变.为提高人脸动画的真实感,将口轮匝肌分解为外圈和内圈两部分,同时建立脸部形变与下颌转动的关系.然后,使用一种实时的特征点跟踪算法跟踪视频中人脸的特征点.最后,将视频跟踪结果转换为肌肉激励参数以驱动人脸动画.实验结果表明,该系统能实时运行,合成的动画也具有较强真实感.与大部分现有的视频驱动的人脸动画方法相比,该系统不需要使用脸部标志和三维扫描设备,极大地方便了用户使用.     

基于深度学习和表情AU参数的人脸动画方法

为了利用计算机方便快捷地生成表情逼真的动漫人物,提出一种基于深度学习和表情AU参数的人脸动画生成方法.该方法定义了用于描述面部表情的24个面部运动单元参数,即表情AU参数,并利用卷积神经网络和FEAFA数据集构建和训练了相应的参数回归网络模型.在根据视频图像生成人脸动画时,首先从单目摄像头获取视频图像,采用有监督的梯度下降法对视频帧进行人脸检测,进而对得到的人脸表情图像准确地回归出表情AU参数值,将其视为三维人脸表情基系数,并结合虚拟人物相对应的24个基础三维表情形状和中立表情形状,在自然环境下基于表情融合变形模型驱动虚拟人物生成人脸动画.该方法省去了传统方法中的三维重建过程,并且考虑了运动单元参数之间的相互影响,使得生成的人脸动画的表情更加自然、细腻.此外,基于人脸图像比基于特征点回归出的表情系数更加准确.     

人脸表情动画

人脸表情动画是计算机动画研究的一个重要分支．它有着十分广泛的应用前景。本文评述了国内外人脸表情动画方法和技术研究现状，提出了进一步需要解决的问题。并针对真人脸表情点提取难题给出了一个有效的特别的解决方案。     

时空人脸表情动画编辑

为了提高计算机合成人脸表情动画的后期制作效率,提出一种基于时空的人脸表情动画编辑方法.首先使用基于拉普拉斯的网格变形技术将用户的编辑效果在空间域传播到整个人脸模型,很好地保留了中性人脸模型的几何细节特征,从而提高合成表情的真实感;然后使用高斯函数将用户编辑效果在时间域传播到邻近表情序列,保持人脸表情动画的平滑过渡,所合成人脸表情动画与初始给定数据保持一致.该方法为用户提供了人脸表情动画编辑的局部控制,可由用户指定编辑的影响范围,使得编辑效果在指定范围内自然传播.实验结果表明,文中方法所合成人脸表情动画自然、真实,有效地提高了数据驱动人脸表情动画的编辑效率.     

人工心理模型驱动的人脸表情动画合成

提出了一种人脸表情动画合成方法,该方法以人工心理模型输出概率值作为权重向量,通过因素加权综合法,控制表情动画模型参数。实验结果表明,该方法实现了心理状态对表情的实时驱动,合成的人脸表情动画真实、自然。     

基于Kinect 的人脸表情捕捉及动画模拟系统研究

三维人脸表情动画技术是一个具有巨大应用前景和研究意义的学科方向。在研究 现有表情捕捉和动画合成技术的基础上,提出了一种基于Microsoft Kinect 设备的人脸表情动画 系统。该系统首先利用Kinect 设备捕捉人脸并提取相关表情参数,同时利用Autodesk Maya 动 画软件建立对应的人脸三维模型,之后将模型导入到OGRE 动画引擎并把提取的表情参数传递 给OGRE,渲染生成实时人脸表情动画。实验结果表明该技术方案可行,实时表情动画效果达 到实际应用水平,相比于现有其他表情动画技术,系统采用通用的Kinect 设备,成本更大众化 且更容易进行操作。     

表演驱动的二维人脸表情合成

为了由视频进行驱动生成人脸表情动画,提出一种表演驱动的二维人脸表情合成方法。利用主动外观模型算法对人脸的关键点进行定位,并从关键点提取出人脸的运动参数;对人脸划分区域,并获取目标人脸的若干样本图像;从人脸的运动参数获取样本图像的插值系数,对样本图像进行线性组合来合成目标人脸的表情图像。该方法具有计算简单有效、真实感强的特点,可以应用于数字娱乐、视频会议等领域。     

利用主动外观模型合成动态人脸表情

人脸表情发生变化时,面部纹理也相应地改变;为了方便有效地模拟这一动态的表情变化过程,提出一种基于主动外观模型的人脸表情合成方法.首先离线学习人脸表情与人脸形状和外观参数之间的关系,利用该学习结果实现对输入人脸图像的表情合成;针对合成图像中眼睛和牙齿模糊的缺点,利用合成的眼睛图像和牙齿模板来替代模糊纹理.实验结果表明,该方法能合成不同表情强度和类型的表情图像;合成的眼睛图像不仅增强了表情的真实感,同时也便于实现眼睛的动画.     

面向纹理特征的真实感三维人脸动画方法

纹理变化是人脸表情的重要组成部分，传统的人脸动画方法通常只是对纹理图像做简单的拉伸变换，没有考虑人脸细微纹理特征的变化，比如皱纹、酒窝等，该文提出了一种面向纹理特征变化的真实感三维人脸动画方法．给出局部表情比率图(Partial Expression Ratio Image，PERI)的概念及其获取方法，在此基础上，进一步给出了面向MPEG-4的PERI参数化与面向三维人脸动画的多方向PERI方法，前者通过有机结合MPEG-4的人脸动画参数(Facial Anlmation Parameter，FAP)，实现人脸动画中细微表情特征的参数化表示；后者通过多方向PERI纹理特征调整方法，使得三维人脸模型在不同角度都具有较好的细微表情特征,该文提出的方法克服了传统人脸动画只考虑人脸曲面形变控制而忽略纹理变化的缺陷，实现面向纹理变化的具有细微表情特征的真实感三维人脸动画，实验表明，该文提出的方法能有效捕捉纹理变化细节，提高人脸动画的真实感。     

基于MPEG-4的人脸表情图像变形研究

为了实时地生成自然真实的人脸表情,提出了一种基于MPEG-4人脸动画框架的人脸表情图像变形方法。该方法首先采用face alignment工具提取人脸照片中的88个特征点;接着在此基础上,对标准人脸网格进行校准变形,以进一步生成特定人脸的三角网格;然后根据人脸动画参数（FAP）移动相应的面部关键特征点及其附近的关联特征点,并在移动过程中保证在多个FAP的作用下的人脸三角网格拓扑结构不变;最后对发生形变的所有三角网格区域通过仿射变换进行面部纹理填充,生成了由FAP所定义的人脸表情图像。该方法的输入是一张中性人脸照片和一组人脸动画参数,输出是对应的人脸表情图像。为了实现细微表情动作和虚拟说话人的合成,还设计了一种眼神表情动作和口内细节纹理的生成算法。基于5分制（MOS）的主观评测实验表明,利用该人脸图像变形方法生成的表情脸像自然度得分为3.67。虚拟说话人合成的实验表明,该方法具有很好的实时性,在普通PC机上的平均处理速度为66.67 fps,适用于实时的视频处理和人脸动画的生成。     

Detail-Aware Deep Clothing Animations Infused with Multi-Source Attributes

This paper presents a novel learning-based clothing deformation method to generate rich and reasonable detailed deformations for garments worn by bodies of various shapes in various animations. In contrast to existing learning-based methods, which require numerous trained models for different garment topologies or poses and are unable to easily realize rich details, we use a unified framework to produce high fidelity deformations efficiently and easily. Specifically, we first found that the fit between the garment and the body has an important impact on the degree of folds. We then designed an attribute parser to generate detail-aware encodings and infused them into the graph neural network, therefore enhancing the discrimination of details under diverse attributes. Furthermore, to achieve better convergence and avoid overly smooth deformations, we proposed to reconstruct output to mitigate the complexity of the learning task. Experimental results show that our proposed deformation method achieves better performance over existing methods in terms of generalization ability and quality of details.     

计算机动画技术综述

本文从关键帧动画、变形物体动画、过程动画、关节动画和人体动画、基于物理的动画几个方面，对计算机动画作了一个较全面的综述.     

浅析中国动画产业与动画教育

随着计算机技术、网络技术的发展,数字动画应运而生,以数字媒体为基础的动画产业成为迅速发展的朝阳产业,也成为国民经济中新的经济增长点,中国的动画产业还处于刚刚起步阶段,而市场对动画人才的需求量很大,这使得动画教育迅猛发展起来。本文分析了中国动画产业与动画教育的现状,提出了如何更好的发展中国动画教育。     

Constructing Human Motion Manifold With Sequential Networks

This paper presents a novel recurrent neural network-based method to construct a latent motion manifold that can represent a wide range of human motions in a long sequence. We introduce several new components to increase the spatial and temporal coverage in motion space while retaining the details of motion capture data. These include new regularization terms for the motion manifold, combination of two complementary decoders for predicting joint rotations and joint velocities and the addition of the forward kinematics layer to consider both joint rotation and position errors. In addition, we propose a set of loss terms that improve the overall quality of the motion manifold from various aspects, such as the capability of reconstructing not only the motion but also the latent manifold vector, and the naturalness of the motion through adversarial loss. These components contribute to creating compact and versatile motion manifold that allows for creating new motions by performing random sampling and algebraic operations, such as interpolation and analogy, in the latent motion manifold.     

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.     

Turbulent Details Simulation for SPH Fluids via Vorticity Refinement

A major issue in smoothed particle hydrodynamics (SPH) approaches is the numerical dissipation during the projection process, especially under coarse discretizations. High‐frequency details, such as turbulence and vortices, are smoothed out, leading to unrealistic results. To address this issue, we introduce a vorticity refinement (VR) solver for SPH fluids with negligible computational overhead. In this method, the numerical dissipation of the vorticity field is recovered by the difference between the theoretical and the actual vorticity, so as to enhance turbulence details. Instead of solving the Biot‐Savart integrals, a stream function, which is easier and more efficient to solve, is used to relate the vorticity field to the velocity field. We obtain turbulence effects of different intensity levels by changing an adjustable parameter. Since the vorticity field is enhanced according to the curl field, our method can not only amplify existing vortices, but also capture additional turbulence. Our VR solver is straightforward to implement and can be easily integrated into existing SPH methods.     

新媒体艺术下动画分类制作与发展趋势研究

动画以其独特的艺术魅力和表现手法风靡了整个世界,并将在新媒体时代展示出更强大的生命力。本文首先介绍了新媒体艺术下计算机动画的分类标准,然后从制作角度分析二维动画、三维动画的技术特性,并对相应的国内外主流动画软件进行了分类对比,最后论文分析了数字动画艺术存在的问题和发展趋势。     

一个用于立体卡通动画的流水模型

提出了一个用于立体卡通动画的流水模型.介绍了如何把手工绘制的卡通流水画面分解成不同的部分,从中提取流水序列的静态信息与动态信息.基于这些信息,建立了一个具有等级结构的模型.通过调整少量的控制参数,模型可以自动生成与手工绘制卡通流水风格一致的动画序列.一个重要的工作是把卡通流水模型扩展到三维,并在CAVE立体投影系统中生成立体卡通流水动画.     

Primitive Object Grasping for Finger Motion Synthesis

We developed a new framework to generate hand and finger grasping motions. The proposed framework provides online adaptation to the position and orientation of objects and can generate grasping motions even when the object shape differs from that used during motion capture. This is achieved by using a mesh model, which we call primitive object grasping (POG), to represent the object grasping motion. The POG model uses a mesh deformation algorithm that keeps the original shape of the mesh while adapting to varying constraints. These characteristics are beneficial for finger grasping motion synthesis that satisfies constraints for mimicking the motion capture sequence and the grasping points reflecting the shape of the object. We verify the adaptability of the proposed motion synthesizer according to its position/orientation and shape variations of different objects by using motion capture sequences for grasping primitive objects, namely, a sphere, a cylinder, and a box. In addition, a different grasp strategy called a three‐finger grasp is synthesized to validate the generality of the POG‐based synthesis framework.     

Animation modeling with petri nets

This paper introduces the use of Petri Nets as a modeling and analysis tool for animation environments. Firstly, the original formulation for Petri Nets is applied in two animation situations, one modeled as a state machine and another exploring interdependent transitions. Increasing the complexity level, some modeling extensions are discussed and more sophisticated animation examples are studied.