UniCon：基于物理的角色运动通用神经控制器
UniCon: Universal Neural Controller For Physics-based Character Motion
摘要：
由于对视频游戏和电影中真实感的需求不断增加，基于物理的动画领域越来越重要，并且最近已经广泛采用数据驱动技术，例如深度强化学习（RL），它从（人类）演示中学习控制。虽然RL在再现个体运动和交互式运动方面表现出令人印象深刻的结果，但现有方法在推广到新运动的能力以及交互式组成复杂运动序列的能力方面受到限制。在本文中，我们提出了一种基于物理的通用神经控制器（UniCon），它通过在大规模运动数据集上学习来学习掌握数千种不同风格的运动。UniCon是一个两级框架，由高级运动调度器和RL驱动的低级运动执行器组成，这是我们的关键创新。通过系统地分析现有的多运动强化学习框架，我们引入了一种新的目标函数和训练技术，使性能有了显著的飞跃。经过培训后，我们的运动执行器可以与不同的高级执行器组合，而无需重新培训，从而实现各种实时交互应用。我们表明，UniCon可以支持键盘驱动的控制，组成运动序列从一个大的游泳池的运动和杂技技巧和心灵传输视频捕捉到的人基于物理的虚拟化身。数值和定性结果表明，UniCon的效率，鲁棒性和可推广性比现有技术有了显着提高，展示了不可见运动，不可见人形模型和不可见扰动的可转移性。
英文摘要：
The field of physics-based animation is gaining importance due to the increasing demand for realism in video games and films, and has recently seen wide adoption of data-driven techniques, such as deep reinforcement learning (RL), which learn control from (human) demonstrations. While RL has shown impressive results at reproducing individual motions and interactive locomotion, existing methods are limited in their ability to generalize to new motions and their ability to compose a complex motion sequence interactively. In this paper, we propose a physics-based universal neural controller (UniCon) that learns to master thousands of motions with different styles by learning on large-scale motion datasets. UniCon is a two-level framework that consists of a high-level motion scheduler and an RL-powered low-level motion executor, which is our key innovation. By systematically analyzing existing multi-motion RL frameworks, we introduce a novel objective function and training techniques which make a significant leap in performance. Once trained, our motion executor can be combined with different high-level schedulers without the need for retraining, enabling a variety of real-time interactive applications. We show that UniCon can support keyboard-driven control, compose motion sequences drawn from a large pool of locomotion and acrobatics skills and teleport a person captured on video to a physics-based virtual avatar. Numerical and qualitative results demonstrate a significant improvement in efficiency, robustness and generalizability of UniCon over prior state-of-the-art, showcasing transferability to unseen motions, unseen humanoid models and unseen perturbation.
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。
