Physically-simulated models for human motion can generate high-quality responsive character animations, often in real-time. Natural language serves as a flexible interface for controlling these models, allowing expert and non-expert users to quickly create and edit their animations. Many recent physics-based animation methods, including those that use text interfaces, train control policies using reinforcement learning (RL). However, scaling these methods beyond several hundred motions has remained challenging. Meanwhile, kinematic animation models are able to successfully learn from thousands of diverse motions by leveraging supervised learning methods. Inspired by these successes, in this work we introduce SuperPADL, a scalable framework for physics-based text-to-motion that leverages both RL and supervised learning to train controllers on thousands of diverse motion clips. SuperPADL is trained in stages using progressive distillation, starting with a large number of specialized experts using RL. These experts are then iteratively distilled into larger, more robust policies using a combination of reinforcement learning and supervised learning. Our final SuperPADL controller is trained on a dataset containing over 5000 skills and runs in real time on a consumer GPU. Moreover, our policy can naturally transition between skills, allowing for users to interactively craft multi-stage animations. We experimentally demonstrate that SuperPADL significantly outperforms RL-based baselines at this large data scale.

翻译：基于物理仿真的人体运动模型能够生成高质量、响应式的角色动画，且通常可实时运行。自然语言为控制这些模型提供了灵活的接口，使专家与非专业用户均能快速创建和编辑动画。许多近期的基于物理的动画方法（包括使用文本接口的方法）通过强化学习训练控制策略。然而，将这些方法扩展到数百种以上动作的训练一直面临挑战。与此同时，运动学动画模型通过利用监督学习方法，已能成功从数千种多样化动作中学习。受此启发，本文提出SuperPADL，一个可扩展的、基于物理的文本到运动生成框架，它结合强化学习与监督学习，在数千个多样化运动片段上训练控制器。SuperPADL采用渐进式蒸馏分阶段训练：首先使用强化学习训练大量专业专家模型，随后通过强化学习与监督学习的组合，将这些专家模型迭代蒸馏为规模更大、鲁棒性更强的策略。我们最终的SuperPADL控制器在包含超过5000种技能的数据集上训练完成，并在消费级GPU上实时运行。此外，我们的策略能够自然地实现技能间的过渡，使用户能够交互式地创作多阶段动画。实验表明，在此大规模数据尺度下，SuperPADL显著优于基于强化学习的基线方法。