Developing systems that can synthesize natural and life-like motions for simulated characters has long been a focus for computer animation. But in order for these systems to be useful for downstream applications, they need not only produce high-quality motions, but must also provide an accessible and versatile interface through which users can direct a character's behaviors. Natural language provides a simple-to-use and expressive medium for specifying a user's intent. Recent breakthroughs in natural language processing (NLP) have demonstrated effective use of language-based interfaces for applications such as image generation and program synthesis. In this work, we present PADL, which leverages recent innovations in NLP in order to take steps towards developing language-directed controllers for physics-based character animation. PADL allows users to issue natural language commands for specifying both high-level tasks and low-level skills that a character should perform. We present an adversarial imitation learning approach for training policies to map high-level language commands to low-level controls that enable a character to perform the desired task and skill specified by a user's commands. Furthermore, we propose a multi-task aggregation method that leverages a language-based multiple-choice question-answering approach to determine high-level task objectives from language commands. We show that our framework can be applied to effectively direct a simulated humanoid character to perform a diverse array of complex motor skills.
翻译:开发能合成模拟字符的自然和类似生命的动作的系统,长期以来一直是计算机动画的焦点。但是,为了让这些系统对下游应用有用,它们不仅需要产生高质量的动作,而且必须提供一个方便和多功能的界面,使用户能够引导性格的行为。自然语言提供了一种简单到使用和表达的媒介,用于说明用户的意图。自然语言处理(NLP)的最近突破表明,在图像生成和程序合成等应用中有效地使用了基于语言的界面。在这项工作中,我们提出了PADLL,它利用了NLP的近期创新,以便采取步骤,为基于物理的性格动画开发语言控制器。PADL允许用户发布自然语言指令,以具体指定高级任务和低级别技能来说明一个用户意图。我们提出了一种对抗性机动性机动性模拟学习方法,用于培训政策,将高级语言指令与低级别控制相匹配,使一个性性能能够执行用户指令所规定的任务和技能。此外,我们提议一种多任务组合组合方法,以便有效地利用一个基于高层次的人类高层次语言指令。