Synthesizing 3D human motion plays an important role in many graphics applications as well as understanding human activity. While many efforts have been made on generating realistic and natural human motion, most approaches neglect the importance of modeling human-scene interactions and affordance. On the other hand, affordance reasoning (e.g., standing on the floor or sitting on the chair) has mainly been studied with static human pose and gestures, and it has rarely been addressed with human motion. In this paper, we propose to bridge human motion synthesis and scene affordance reasoning. We present a hierarchical generative framework to synthesize long-term 3D human motion conditioning on the 3D scene structure. Building on this framework, we further enforce multiple geometry constraints between the human mesh and scene point clouds via optimization to improve realistic synthesis. Our experiments show significant improvements over previous approaches on generating natural and physically plausible human motion in a scene.
翻译:合成3D人类运动在许多图形应用以及理解人类活动方面起着重要作用。虽然在产生现实和自然的人类运动方面已经做出了许多努力,但大多数方法忽视了模拟人类-环境相互作用和富足性的重要性。另一方面,用静态的人类姿势和手势(例如,站在地板上或坐在椅子上)来研究原始推理(例如,站在椅子上)主要是用静态的人类姿势和手势来研究,很少用人类运动来讨论。在本文件中,我们提议将人类运动合成和场景的推理结合起来。我们提出了一个等级分级的遗传框架,以合成3D场结构上的长期3D人类运动调节。在这个框架内,我们进一步通过优化在人类网状和场景点云间实施多种几何限制,以改进现实的合成。我们的实验表明,在以往的场景中产生自然和物理上可信的人类运动的方法有了重大改进。