论文标题
观看您的步骤:实时自适应角色步进
Watch Your Step: Real-Time Adaptive Character Stepping
论文作者
论文摘要
一种有效的3D步进控制算法,在计算上快速,健壮且易于实现对角色动画研究非常重要且有价值。在本文中,我们提出了一种新颖的技术,用于产生动态,互动和可控的双头阶梯动作。我们的方法使用低维物理学的模型来创建平衡的人形化头像,这些化身可以处理各种互动情况,例如地形高度移动和推动劳累,同时保持直立和平衡。我们通过将流行的倒置模式与脚踝反馈扭矩和可变腿长的机制相结合,以创建可控的解决方案来实现这一目标,该解决方案可以实时适应不可预见的情况,而无需密钥框架数据,任何离线预处理或在线优化扭矩计算的任何离线预处理。我们与基本IP模型以及通过其他控制机制扩展模型的原因解释和解决了过度的简化和局限性。我们展示了一种简单而快速的方法,用于扩展基于踝关节和可变的腿长近似值的IP模型,而不会阻碍极具吸引力的属性(即计算速度,鲁棒性和简单性),使IP模型非常理想地产生响应式响应式响应式响应的平衡平衡动作。最后,尽管我们的技术着重于下半身的运动,但即使在地形高度变化期间,它也可以处理小型和大型推力。此外,我们的模型有效地创造了类似人类的动作,从而综合了低级直立的步进运动,并且可以与其他控制器技术结合使用以产生全身自主剂。
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.