We present PACE, a novel method for modifying motion-captured virtual agents to interact with and move throughout dense, cluttered 3D scenes. Our approach changes a given motion sequence of a virtual agent as needed to adjust to the obstacles and objects in the environment. We first take the individual frames of the motion sequence most important for modeling interactions with the scene and pair them with the relevant scene geometry, obstacles, and semantics such that interactions in the agents motion match the affordances of the scene (e.g., standing on a floor or sitting in a chair). We then optimize the motion of the human by directly altering the high-DOF pose at each frame in the motion to better account for the unique geometric constraints of the scene. Our formulation uses novel loss functions that maintain a realistic flow and natural-looking motion. We compare our method with prior motion generating techniques and highlight the benefits of our method with a perceptual study and physical plausibility metrics. Human raters preferred our method over the prior approaches. Specifically, they preferred our method 57.1% of the time versus the state-of-the-art method using existing motions, and 81.0% of the time versus a state-of-the-art motion synthesis method. Additionally, our method performs significantly higher on established physical plausibility and interaction metrics. Specifically, we outperform competing methods by over 1.2% in terms of the non-collision metric and by over 18% in terms of the contact metric. We have integrated our interactive system with Microsoft HoloLens and demonstrate its benefits in real-world indoor scenes. Our project website is available at https://gamma.umd.edu/pace/.
翻译:我们提出了PACE,一种新颖的方法,用于修改运动捕获的虚拟人,使其与并穿越密集、杂乱的3D场景相互作用。我们的方法通过需要时更改虚拟人的给定运动序列,以适应环境中的障碍物和对象。我们首先获取最重要的用于模拟与场景交互的虚拟人运动序列的各个帧,并将其与相关场景几何、障碍物和语义配对,以使虚拟人的运动与场景的适应性相匹配(例如,站在地板上或坐在椅子上)。然后,我们通过在每个帧处直接改变高DOF姿势的形式优化人类的运动,以更好地考虑场景的唯一几何约束。我们的公式使用新颖的损失函数来保持逼真的流动和自然的动作。我们将我们的方法与先前的运动生成技术进行了比较,并用感知研究和物理合理性指标突出了我们方法的好处。与先前的方法相比,人类评定者更喜欢我们的方法。具体而言,他们有57.1%的时间更喜欢我们的方法而不是使用现有运动的最先进方法,而有81.0%的时间更喜欢我们的方法而不是最先进的运动合成方法。此外,我们的方法在建立的物理可信性和交互指标上表现显着更高。具体而言,在非碰撞指标方面,我们超过竞争方法超过1.2%,在接触指标方面超过18%。我们已将我们的交互式系统集成到Microsoft HoloLens中,并在现实世界的室内场景中展示了它的优势。我们的项目网站位于https://gamma.umd.edu/pace/。