Learning compact and efficient deformable models of human shape variation

我们提出的主要贡献是研究创新的组明智的方法来学习紧凑和高效的人体形状变化的可变形模型，并检查其有效性，提高无标记运动捕捉系统的精度。特别新颖的兴趣是使用这种方法来协助运动学分析的个人参与体育activity.The问题的有效恢复的姿态（3D位置和方向）的人体部位使用视觉无标记的意见是一个有趣的问题，由于其理论的重要性和潜在的不同用途。这是一个问题，人类视觉系统表现出显着的能力，似乎毫不费力地解决这个复杂的问题，然而，一个仍然是一个非常困难的任务，计算机复制具有相同的精度水平。一个广泛的有用的应用可以实现提供这个基本问题是鲁棒和有效地解决，特别是运动捕捉系统。传统上，它们采用光学标记和/或其他专用硬件来解决这个问题，并且被广泛使用，特别是在娱乐行业。然而，大量的文献存在，这是致力于实时恢复的姿态为无标记的关节对象，如人体，衣服，和人造物体。开发基于无标记计算机视觉的解决方案的高度兴趣是由于它们是非侵入性的，更灵活的，并且可能比基于侵入性光学标记的解决方案更便宜。为了提高这些解决方案的性能，学习通用变形模型捕获人类形状变化，包括复杂的非刚性变形和关节，已被证明是特别有用的。一种常见的方法试图参数化的数据表示的整个形状空间中的一个较低的维度的子空间的形式，在其中的可变性的对象类从模板或平均shape被encoded.Despite一些缺点，光学标记为基础的技术仍然是电影和视频游戏行业的动作捕捉的支柱。这是由于大多数当前的方法是不精确的，仅限于与单个个体一起工作，并且当与场景中的外围物品（例如道具或运动器材）交互时无法科普。为了使未来的无标记系统更加成功，减少对各种假设的依赖，学习高度详细的形状模型提供了一个可靠的研究方向。

Our main proposed contribution is to investigate innovative group-wise approaches to learning compact and efficient deformable models of the human shape variation, and to examine their effectiveness in improving the precision of markerless motion capture systems. Of particular novel interest is the use of such approaches to assist in the kinematic analysis of individuals participating in sporting activities.The problem of efficiently recovering the pose (3D position and orientation) of human body parts using visual markerless observations is an interesting problem, due to its theoretical importance and its potential diverse uses. It is a problem which the human visual system exhibits a remarkable ability to seemingly effortlessly solve this complex problem, however one which remains an extremely difficult task for a computer to replicate with the same level of accuracy.A wide range of useful applications can be implemented provided that this fundamental problem is robustly and efficiently solved, in particular motion capture systems. Traditionally, they employ optical markers and / or other specialized hardware to tackle this problem and are widely used especially in the entertainment industry. However, a large body of literature exists which is devoted to real-time recovery of pose for markerless articulable objects, such as human bodies, clothes, and man-made objects. The high level of interest in developing markerless computer-vision based solutions is due to the fact that they are non-invasive, more flexible and potentially cheaper than solutions based on intrusive optical marker based systems.In order to improve the performance of these solutions, learned generic morphable models which capture human shape variation, including complex non-rigid deformations and articulation, have been shown to be particularly useful. A common approach attempts to parametrization the entire shape space represented by the data in the form of a lower dimensional subspace, in which the variability of an object class from a template or mean shape is encoded.Despite a number of drawbacks, optical-marker based techniques remain the mainstay for motion capture for the film and video-game industries. This is due to most current approaches being imprecise, limited to working with single individuals and unable to cope when there are interactions with peripheral items in the scene, such as props or sporting equipment. For future markerless systems to be more successful and less dependent on various assumptions, learning highly detailed shape models provides a credible research direction.