注意你的步伐：实时的自适应字符步进
Watch Your Step: Real-Time Adaptive Character Stepping
摘要：
一种有效的、计算速度快、鲁棒性强、易于实现的三维步进控制算法对角色动画的研究具有重要的意义。在本文中，我们提出了一种新的技术，用于生成动态的，交互式的，可控的步进运动。我们的方法使用低维基于物理的模型来创建平衡的人形化身，可以处理各种交互情况，例如地形高度移动和推动用力，同时保持直立和平衡。我们通过将流行的倒立摆模型与踝关节反馈扭矩和可变腿长机制相结合来实现这一点，以创建一个可控的解决方案，该解决方案可以实时的【形】适应不可预见的情况，而无需关键帧数据，任何离线预处理或在线优化关节扭矩计算。我们解释和解决过度简化和限制的基本IP模型和扩展模型的原因，通过额外的控制机制。我们展示了一种简单而快速的方法，用于扩展基于踝关节扭矩和可变腿长近似的IP模型，而不会阻碍非常有吸引力的特性（即，计算速度，鲁棒性和简单性），这使得IP模型非常适合用于生成直立响应的平衡运动。最后，虽然我们的技术侧重于下半身运动，但它可以处理小和大的推力，即使在地形高度变化。此外，我们的模型有效地创建类人的运动，合成低层次的直立踏步运动，并可以与额外的控制器技术相结合，产生全身自主智能体。
英文摘要：
An effective 3D stepping control algorithm that is computationally fast, robust, and easy to implement is extremely important and valuable to character animation research. In this paper, we present a novel technique for generating dynamic, interactive, and controllable biped stepping motions. Our approach uses a low-dimensional physics-based model to create balanced humanoid avatars that can handle a wide variety of interactive situations, such as terrain height shifting and push exertions, while remaining upright and balanced. We accomplish this by combining the popular inverted-pendulum model with an ankle-feedback torque and variable leg-length mechanism to create a controllable solution that can adapt to unforeseen circumstances in real-time without key-framed data, any offline pre-processing, or on-line optimizations joint torque computations. We explain and address oversimplifications and limitations with the basic IP model and the reasons for extending the model by means of additional control mechanisms. We demonstrate a simple and fast approach for extending the IP model based on an ankle-torque and variable leg lengths approximation without hindering the extremely attractive properties (i.e., computational speed, robustness, and simplicity) that make the IP model so ideal for generating upright responsive balancing biped movements. Finally, while our technique focuses on lower body motions, it can, nevertheless, handle both small and large push forces even during terrain height variations. Moreover, our model effectively creates human-like motions that synthesize low-level upright stepping movements, and can be combined with additional controller techniques to produce whole body autonomous agents.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
