Movement is how people interact with and affect their environment. For realistic character animation, it is necessary to synthesize such interactions between virtual characters and their surroundings. Despite recent progress in character animation using machine learning, most systems focus on controlling an agent's movements in fairly simple and homogeneous environments, with limited interactions with other objects. Furthermore, many previous approaches that synthesize human-scene interactions require significant manual labeling of the training data. In contrast, we present a system that uses adversarial imitation learning and reinforcement learning to train physically-simulated characters that perform scene interaction tasks in a natural and life-like manner. Our method learns scene interaction behaviors from large unstructured motion datasets, without manual annotation of the motion data. These scene interactions are learned using an adversarial discriminator that evaluates the realism of a motion within the context of a scene. The key novelty involves conditioning both the discriminator and the policy networks on scene context. We demonstrate the effectiveness of our approach through three challenging scene interaction tasks: carrying, sitting, and lying down, which require coordination of a character's movements in relation to objects in the environment. Our policies learn to seamlessly transition between different behaviors like idling, walking, and sitting. By randomizing the properties of the objects and their placements during training, our method is able to generalize beyond the objects and scenarios depicted in the training dataset, producing natural character-scene interactions for a wide variety of object shapes and placements. The approach takes physics-based character motion generation a step closer to broad applicability.
翻译:对于现实的性格动画来说,有必要将虚拟人物及其周围环境之间的这种互动结合起来。尽管最近通过机器学习在性动动动中取得了进步,但大多数系统都侧重于在非常简单和单一的环境中控制一个代理人的动作,与其他对象的互动有限。此外,许多以前综合人-生理相互作用的方法都需要对培训数据进行大量的手工标签。相比之下,我们展示了一个系统,利用对立模拟学习和强化学习来培训物理模拟的人物,这些人物以自然和类似生命的方式执行现场互动任务。我们的方法从大型非结构化运动数据集中学习现场互动行为,而没有手动说明运动数据。这些场面互动是利用对立性分析器来学习的,用来评价在场面背景下运动的现实主义。关键的新概念涉及调整歧视者和现场政策网络。我们通过三种具有挑战性的场面互动任务来展示我们的方法的有效性:执行、坐着和躺着,这需要协调与环境中物体有关的性能运动的动作动作。我们的政策通过一个对抗性动作的动作来学习,在一般的培训对象中进行无缝的动作的动作的动作,比如在演练过程中进行无缝的动作。