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：运动是人们与环境互动并影响其环境的方式。为实现逼真的角色动画，必须合成虚拟角色与其周围环境之间的此类交互。尽管近年来机器学习在角色动画领域取得了进展，但大多数系统仅关注在相对简单且同质的环境中控制代理的运动，且与其他对象的交互有限。此外，许多先前合成人类-场景交互的方法需要对训练数据进行大量手动标注。相比之下，我们提出了一种系统，利用对抗模仿学习和强化学习来训练物理模拟角色，使其以自然且逼真的方式执行场景交互任务。我们的方法从大规模非结构化运动数据集中学习场景交互行为，无需对运动数据进行手动标注。这些场景交互通过一个对抗性判别器进行学习，该判别器评估运动在场景上下文中的逼真度。关键创新在于将判别器和策略网络都条件化为场景上下文。我们通过三个具有挑战性的场景交互任务（搬运、坐下和躺下）证明了方法的有效性，这些任务需要协调角色相对于环境中物体的运动。我们学习的策略能够无缝地在闲逛、行走和坐下等不同行为之间转换。通过在训练过程中随机化物体的属性及其放置位置，我们的方法能够泛化到训练数据集中所描绘的物体和场景之外，从而为各种物体形状和放置位置生成自然的角色-场景交互。该方法将基于物理的角色运动生成向广泛应用迈出了重要一步。