低复杂度的H.264帧间宏块编码模式选择算法

为了减少H．264／AVC帧间编码模式选择的计算复杂度，利用不同编码模式在实际视频序列中的利用率信息及空时域相邻块之间的相关性，提出了一个低复杂度的快速帧间编码模式选择算法。模拟结果表明，该算法在保持率失真性能的前提下可以大幅度减少模式选择的计算复杂度，有利于H．264的实时应用。     

一种基于块匹配的运动估计改进算法

运动估计是视频压缩中帧间预测编码的关键技术之一,在各个压缩标准中都广泛使用了基于块的运动估计技术。由于运动估计通常具有较大的运算量,因此对压缩性能具有重要的影响。本文在分析了现有的六边形搜索算法(HEXBS)和视频序列图像帧相邻空间块和对应的运动向量具有高度空间相关性的基础上提出了一种改进的快速运动估计搜索算法,通过实验表明与HEXBS算法具有相似的计算复杂度,但是视频编码质量优于HEXBS算法。     

改进的H.264帧间模式选择算法

针对H.264帧间模式选择算法存在的问题,提出一种帧间模式选择快速算法.算法采用8×8块运动矢量同质性分析对宏块模式进行选择,缩小候选模式范围,降低H.264帧间模式选择算法的计算复杂度;使用模式相关和运动矢量合并对UMHexagonS算法进行优化,减少大量的运动估计搜索点数,取得了较高的编码效率.实验结果表明,与使用UMHexagonS的帧间模式选择算法相比,该算法在峰值信噪比及输出码流码率基本不变的前提下,可以平均减少大约50％的运动估计时间.     

视频序列的全景图拼接技术

提出了一种对视频序列进行全景图拼接的方法。主要讨论了有大面积的非刚性运动物体出现的序列，不过此方法也同样适用于无运动物体的纯背景序列。为计算各帧间的投影关系，用仿射模型来描述摄像机运动，并用特征点匹配的方法计算出模型中各参数的值。由于用相关法计算的匹配结果准确率比较低，所以用RANSAC（Random Sampling Consensus）对匹配结果进行了筛选，可以准确求出摄像机运动参数。利用运动参数进行投影，然后用多帧相减并求交集，估计出每帧图像中运动物体存在的区域，最后计算得到了全景图。该方法的结果与前人得到的结果进行了比较，证明用此方法能获得质量较高的全景图。     

H.264帧间预测模式选择的快速算法优化

在H.264视频编码器帧内预测模式下,为确定一个宏块的帧内预测模式,会带来很大的运算复杂度,基于图像差分的帧间预测模式快速选择算法,采用测定图像序列平坦度的思想,能够实现快速帧间模式选择。可以有效减少模式搜索次数并能保证视频编码的质量,从而可大幅度降低编码时间和视频压缩的计算量,有效地减少了帧间模式选择算法运算复杂度,有利于实际应用。     

联合分数运动估计优化的多参考帧快速选择算法

多参考帧和分数运动补偿技术虽然提高了视频编码性能,但同时也使编码器的内存消耗和计算复杂度成倍地增加.其主要原因是多参考帧技术令会运动估计重复地应用在每个参考帧上.通过分析这两种技术对编码性能贡献的相关性,提出了一种联合优化的快速算法.首先,根据不同参考帧之间的合成运动向量,选择空间上最接近的候选参考帧.然后,通过二维多项式模型预测最优分数残插值,决定采用分数运动估计的参考帧.实验结果表明,该方法在不损失任何率失真性能的情况下,可以把计算的复杂度减少为原来的1/3.     

基于H.264的快速运动估计算法

运动估计在视频编码中有着举足轻重的地位,其性能的优劣在很大程度上影响着输出码流的质量.为了克服视频编码标准H.264中运动估计运算量大的困难,在深入研究H.264编码标准的基础上,利用视频图像序列的帧间统计特性,提出了一种改进的非对称十字型多层次六边形格点搜索算法来搜索最佳运动矢量.实验结果表明,该算法对于各种运动序列,在保持编码性能不受损失的同时,可以使得运动估计的运算复杂度大为降低.     

基于帧间预测模式的插值滤波器

为了进一步降低计算复杂度,提高视频图像的质量,通过对帧间预测模式的运动补偿分析及图像的频谱分析,提出了一种基于帧间预测模式的插值滤波器算法.该算法对每个不同的分块编码模式使用不同阶次的插值滤波器,为每一个分块编码模式选择最优的插值滤波器.实验结果表明,该算法不仅能有效地降低计算复杂度,还能提高信噪比,使图像的质量得到进一步的改善.     

构造二叉树的两个改进算法

在数据结构中,已知一棵二叉树的先序序列和中序序列,可唯一确定此二叉树.本文在分析建立二叉树经典算法的时间复杂度的基础上,给出了两个改进算法:①利用哈希函数,使得改进后的算法在最差情况下,时间复杂度由O(n2)降为O(n);②利用栈和控制输入的结点序列构造二叉树,时间复杂度也由O(n2)降为O(n).     

H.264/AVC快速帧间模式选择算法研究

H.264/AVC标准采用可变块运动估计和率失真优化技术进行帧间模式选择,提高了编码效率,但极大地增加了计算复杂度。为了解决这一问题,提出一种快速帧间模式选择算法,它利用了SKIP模式在视频序列中的使用率信息和时空域相邻宏块与当前宏块编码模式之间的高度相关性。该算法缩小了模式选择的范围,并采用阈值来提高预测准确度。仿真结果表明,该算法在保持图像质量和比特率的前提下可以大幅度减少计算复杂度。     

基于小波变换的运动分析及其应用

对捕获的运动数据进行编辑处理 ,是生成新的复杂人体动画和提高运动捕获数据重用性的关键 ,但目前大多数运动编辑技术不具备对运动进行高层控制处理的能力 ,为此 ,提出了一种基于小波变换的运动编辑新算法 ,即将小波变换引入运动编辑 ,并对运动信号进行多分辨率分析 ,从而实现了运动特征增强、运动融合及运动特征提取与综合 .实验结果表明 ,该算法非常适合对运动特征进行处理 ,由于其能够在高层次上对运动进行有效的编辑 ,因而提高了动画师的工作效率 .     

Data‐driven detailed hair animation for game characters

We propose a data‐driven method to realize high‐quality detailed hair animations in interactive applications like games. By devising an error metric method to evaluate hair animation similarities, we take hair features into consideration as much as possible. We also propose a novel database construction algorithm based on Secondary Motion Graph. Our algorithm can improve the efficiency of such graphs to reduce redundant data and also achieves visually smooth connection of two animation clips while taking into consideration their future motions. The costs for the run‐time process using our Secondary Motion Graph are relatively low, allowing real‐time interactive operations. Copyright © 2016 John Wiley & Sons, Ltd.     

Efficient local transformation estimation using Lie operators

Conventional translation-only motion estimation algorithms cannot cope with transformations of objects such as scaling, rotations and deformations. Motion models characterizing non-translation motions are thus beneficial as they offer more accurate motion estimation and compensation. In this paper, we introduce low-complexity transformation estimation methods with four motion models based on Lie operators, which are linear operators that have found applications in optical character recognitions. We show that individual Lie operators are capable of capturing small degrees of object transformations. We propose an efficient local transformation estimation algorithm in order to further improve the accuracy of the translation-only estimation by integrating all four motion models. Simulations with an MPEG-2 video codec on two video sequences show that the proposed transformation estimation approach can noticeably improve the motion compensation performance of the translation-only method by achieving higher PSNR (peak signal-to-noise ratio) values for the predicted frames, with only a small fraction of the complexity required by the translation motion search.     

结合低维运动模型和逆运动学的风格化人体运动合成

如何得到满足用户指定约束的风格化运动是近年来计算机动画领域的研究难点,针对这个问题,提出一个基于独立特征子空间的低维运动模型,该模型可以较好地参数化运动风格,并在此基础上提出了一种合成满足约束的风格化人体运动的算法.该算法在低维空间中求解反向运动学问题,并在风格子空间中响应用户输入的风格参数,使用户可以在指定关键帧末端约束的同时对风格进行编辑.实验结果表明:文中算法效率高,具有良好的交互性,能够用于动画的交互式编辑和与合成.     

Hybrid motion graph for character motion synthesis

ObjectiveThis paper proposes a novel framework of Hybrid Motion Graph (HMG) for creating character animations, which enhances the graph-based structural control by motion field representations for efficient motion synthesis of diverse and interactive character animations.MethodsIn HMG framework, the motion template of each class is automatically derived from the training motions for capturing the general spatio-temporal characteristics of an entire motion class. Typical motion field for each class is then constructed. The smooth transitions among motion classes are then generated by interpolating the related motion templates with spacetime constraints. Finally, a hybrid motion graph is built by integrating the separate motion fields for each motion class into the global structural control of motion graph through smooth transition.ResultsIn motion synthesis stage, a character may freely ‘switch’ among different motion classes in the hybrid motion graph via smooth transitions between motion templates and ‘flow’ within each class through the continuous space of motion field with agile and the continuous control process.ConclusionExperimental results show that our framework realizes the fast connectivity among different motion classes and high responsiveness and interactivity for creating realistic character animation of rich behaviors with limited motion data and computational resources.     

Perception-based model simplification for motion blur rendering

Motion blur effects are important to motion perception in visual arts, interactive games and animation applications. Usually, such motion blur rendering is quite time consuming, thus blocking the online/interactive use of the effects. Motivated by the human perception in relation to moving objects, this paper presents simplified geometric models that enable to speedup motion blur rendering, which has not been tracked in motion blur rendering specifically. We develop a novel algorithm to simplify models with motion-aware, to preserve the features whose characteristics are perceivable in motion. We deduce the formula to outline the level of detail simplification by the object moving velocity. Using our simplified models, methods for motion blur rendering can achieve the rendering quality as using the original models, and obtain the processing acceleration mostly. The experimental results have shown the effectiveness of our approach, more acceleration with the larger models or faster motion (e.g. for the dragon model with over a million facets, the motion-blur rendering via hierarchical stochastic rasterization is sped up by over 27 times).     

基于球面参数化的人脸动画重映射

动画重映射是生成真实感人脸动画的重要途径,为了快速地进行人脸动画重映射,提出了一种基于球面参数化的动画重映射算法,该算法先通过引入球面参数化来保证参数域上的三角面不重叠,再利用重心坐标插值,以实现简单、无歧义的重映射。该算法不仅能够在保证真实感的基础上显著提升重映射的速度,并可减少需要标记的关键点个数。另外,结合唇线自动分割和区域划分还能修正直接插值产生的错误。实验表明,该新算法可用于Motion Capture采集的稀疏关键点的人脸动画数据的重映射。     

基于优化的DTW算法的人体运动数据检索

随着大量三维人体运动数据库的建立,使得在数据库中实现基于内容的三维人体运动检索面临着诸多困难,文中提出一种分阶段的动态时间变形(DTW)优化算法的人体运动数据检索技术,可有效检索出逻辑上相似的运动。该算法首先对齐两个运动序列的坐标位置,基于窗口距离构造距离矩阵。其次采用基于全局和局部约束的DTW优化算法进行相似度匹配,得到两个运动间的对应关系。最后通过归一化相似度和DTW平均距离分阶段判断运动的相似性。实验结果表明,分阶段的DTW优化算法在提高效率的同时对长度不等的运动能取得较好的检索结果。     

Intuitive Facial Animation Editing Based On A Generative RNN Framework

For the last decades, the concern of producing convincing facial animation has garnered great interest, that has only been accelerating with the recent explosion of 3D content in both entertainment and professional activities. The use of motion capture and retargeting has arguably become the dominant solution to address this demand. Yet, despite high level of quality and automation performance-based animation pipelines still require manual cleaning and editing to refine raw results, which is a time- and skill-demanding process. In this paper, we look to leverage machine learning to make facial animation editing faster and more accessible to non-experts. Inspired by recent image inpainting methods, we design a generative recurrent neural network that generates realistic motion into designated segments of an existing facial animation, optionally following user-provided guiding constraints. Our system handles different supervised or unsupervised editing scenarios such as motion filling during occlusions, expression corrections, semantic content modifications, and noise filtering. We demonstrate the usability of our system on several animation editing use cases.