Directable Animation

直接动画

• 批准号: 560306-2020
• 负责人: vandePanne, Michiel
• 金额: $ 2.91万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=721753
• 关键词: Directable; Animation

Animation is a creative endeavor that relies heavily on available tools and workflows, with keyframing being at the heart of much of traditional animation and computer animation. Recently, new methods are increasingly leveraging machine learning and physics to create animated movement via predictive modeling. However, many of these approaches some from the machine learning community and focus on automated solutions, with little attention being paid to giving animators sufficient control over the motion. In this project, we seek to develop methods that exploit the benefits of physics and data-driven predictive methods, while also allowing for animator directability.We pursue three specific approaches. First, we will investigate how keyframing can leverage passive physics-based simulation in order to easily produce natural contact and collision behaviors, as applied to characters interacting with their environment. Second, we explore and extend data-driven solutions to keyframing-based motion completion problems. Third, we investigate methods that enable animators to better leverage deep reinforcement learning to produce physics-based characters with controllable motion styles and motion variations. Taken together, these three tackle promising way of integrating some of the creative demands of artists and animators with the capabilities afforded by predictive models.

动画是一项创造性的奋进，在很大程度上依赖于可用的工具和工作流程，关键帧是许多传统动画和计算机动画的核心。最近，新方法越来越多地利用机器学习和物理学来通过预测建模创建动画运动。然而，其中许多方法来自机器学习社区，专注于自动化解决方案，很少关注动画师对运动的充分控制。在这个项目中，我们寻求开发方法，利用物理和数据驱动的预测方法的好处，同时也允许动画师的方向性。我们追求三个具体的方法。首先，我们将研究关键帧如何利用基于被动物理的模拟，以便轻松产生自然的接触和碰撞行为，应用于角色与环境的交互。其次，我们探索和扩展数据驱动的解决方案，以关键帧为基础的运动完成问题。第三，我们研究了使动画师能够更好地利用深度强化学习来生成具有可控运动风格和运动变化的基于物理的角色的方法。总的来说，这三个解决了将艺术家和动画师的一些创造性需求与预测模型提供的能力相结合的有希望的方法。