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群体智慧方法来学习人体形状变化的紧凑和有效的可变形模型，并检查它们在提高无标记运动捕捉系统精度方面的有效性。特别新奇的是使用这种方法来协助个人参加体育活动的运动学分析。由于其理论重要性和潜在的多种用途，使用视觉无标记观察有效地恢复人体部位的姿势（3D位置和方向）的问题是一个有趣的问题。这是一个问题，人类的视觉系统显示出一种非凡的能力，似乎毫不费力地解决这个复杂的问题，然而，对于计算机来说，以同样的精度复制这个问题仍然是一项极其困难的任务。广泛的有用的应用可以实现提供这个基本问题是稳健和有效地解决，特别是运动捕捉系统。传统上，它们使用光学标记和/或其他专门的硬件来解决这个问题，并被广泛使用，特别是在娱乐行业。然而，有大量的文献致力于对无标记可表达物体（如人体、衣服和人造物体）的姿势进行实时恢复。开发基于计算机视觉的无标记解决方案的高度兴趣是由于它们是非侵入性的，比基于侵入性光学标记系统的解决方案更灵活，并且可能更便宜。为了提高这些解决方案的性能，学习的通用变形模型捕获人体形状变化，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以低维子空间的形式对数据表示的整个形状空间进行参数化，其中对对象类与模板或平均形状的可变性进行编码。尽管存在许多缺点，基于光学标记的技术仍然是电影和视频游戏行业动作捕捉的主要技术。这是由于目前大多数方法都不精确，仅限于与单个个体一起工作，并且无法处理与场景中的外围物品（如道具或运动器材）的交互。为了使未来的无标记系统更成功，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.