Authoring an appealing animation for a virtual character is a challenging task. In computer-aided keyframe animation artists define the key poses of a character by manipulating its underlying skeletons. To look plausible, a character pose must respect many ill-defined constraints, and so the resulting realism greatly depends on the animator's skill and knowledge. Animation software provide tools to help in this matter, relying on various algorithms to automatically enforce some of these constraints. The increasing availability of motion capture data has raised interest in data-driven approaches to pose design, with the potential of shifting more of the task of assessing realism from the artist to the computer, and to provide easier access to nonexperts. In this article, we propose such a method, relying on neural networks to automatically learn the constraints from the data. We describe an efficient tool for pose design, allowing na{\"i}ve users to intuitively manipulate a pose to create character animations.

中文翻译：

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用于高效交互设计的基于学习的姿势编辑

为虚拟角色创作吸引人的动画是一项具有挑战性的任务。在计算机辅助关键帧动画中，艺术家通过操纵其底层骨架来定义角色的关键姿势。为了看起来合理，角色姿势必须遵守许多定义不明确的约束，因此产生的真实感在很大程度上取决于动画师的技能和知识。动画软件提供工具来帮助解决这个问题，依靠各种算法来自动强制执行其中一些约束。动作捕捉数据的日益普及引起了人们对数据驱动的姿势设计方法的兴趣，有可能将更多评估真实感的任务从艺术家转移到计算机，并为非专家提供更容易的访问。在这篇文章中，我们提出了这样一种方法，依靠神经网络从数据中自动学习约束。我们描述了一种用于姿势设计的有效工具，它允许新手用户直观地操纵姿势来创建角色动画。