CCF: Capturing and Animating the Human Hand: Robust Recovery of Hand-Object Interactions

国家合作框架：捕捉和动画的人手：手对象交互的鲁棒恢复南希Pollard摘要这项研究解决了准确捕捉人手的运动的问题。 由于各种原因，捕获人手运动已被证明是非常困难的，包括手中关节的复杂性、人手解剖结构的因人而异、小范围的运动、与运动捕获技术相关的困难以及操纵对象时的复杂接触条件。 然而，手对于沟通，关心他人和自己以及使用工具改变我们周围的世界至关重要。 这项研究涵盖了一套实用的技术，使准确的，特定于主题的捕捉人手可能使用今天的硬件。 这些技术连同相应的手部运动数据库沿着将使得在以高细节和精确度水平利用捕获的数据执行各种任务时研究手部的工作成为可能。这项工作应该是有用的康复措施，在改善运动范围和完成日常活动的进展。这对探索机器人手的潜在设计是有用的。本研究涉及三个具体的子课题。第一个是从运动捕捉数据中自动提取特定对象骨架模型的鲁棒算法。然后，我们观察到标记协议对结果有很大影响。因此，本研究的第二个子主题是获得有根据的建议，标记协议的准确数据采集。接下来，我们观察到，清洁的运动捕捉数据没有捕捉到接触条件，这是非常重要的理解把握。因此，本研究的第三个子主题是开发优化算法，以获得物理上合理的表示所观察到的运动，即使在复杂的情况下，不断变化的手和操纵对象的可变形组织之间的接触。

CCF: Capturing and Animating the Human Hand: Robust Recovery of Hand-Object InteractionsNancy PollardAbstractThis research addresses the problem of accurately capturing motion of the human hand. Capturing human hand motion has proven exceptionally difficult for a variety of reasons, including the complexity of the joints in the hand, variation in hand anatomy from person to person, small ranges of motion, difficulties related to motion capture technology, and complex contact conditions while manipulating objects. However, the hand is critically important for communication, caring for others and ourselves, and using tools to alter the world around us. This research covers a suite of practical techniques needed to make accurate, subject-specific capture of the human hand possible using today's hardware. These techniques, along with the corresponding hand motion database, will make it possible to study the workings of the hand when performing all manner of tasks with captured data at a high level of detail and accuracy. This work should be useful in rehabilitation for measuring progress in improving range of motion and in accomplishing everyday activities. It should be useful for exploring potential designs for robot hands. And it should also be of great use for exploring the basis of dexterity itself.This research involves three specific subtopics. The first is robust algorithms for automatic extraction of subject specific skeletal models from motion capture data. We then observe that marker protocol affects the results a great deal. Thus, the second subtopic of this research is obtaining well founded recommendations for marker protocols for accurate data capture. Next we observe that cleaned motion capture data does not capture the contact conditions that are so important to understanding grasping. Thus, the third subtopic of this research is developing optimization algorithms to obtain physically plausible representations of observed motion, even in situations with complex, changing contacts between deformable tissues of the hand and a manipulated object.