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PACE，一种用于修改运动捕捉虚拟代理以使其在密集杂乱的3D场景中交互和移动的新方法。我们的方法根据环境中的障碍物和物体，按需修改虚拟代理的给定运动序列。首先，我们提取运动序列中对建模场景交互最重要的单独帧，并将其与相关场景几何、障碍物和语义进行配对，使得代理运动中的交互与场景的 affordances（例如，站在地板上或坐在椅子上）相匹配。然后，通过直接改变运动中每一帧的高自由度姿态来优化人体运动，以更好适应场景的独特几何约束。我们的公式使用新颖的损失函数，以保持逼真的流动性和自然的运动。我们将我们的方法与先前的运动生成技术进行比较，并通过感知研究和物理真实性指标突出我们方法的优势。人类评分者更偏好我们的方法而非先前方法。具体而言，与使用现有运动的最先进方法相比，他们在 57.1% 的情况下偏好我们的方法；与最先进的运动合成方法相比，这一比例为 81.0%。此外，我们的方法在已建立的物理真实性和交互指标上表现显著更高。具体来说，在无碰撞指标上我们优于竞争方法超过 1.2%，在接触指标上超过 18%。我们已将交互系统与 Microsoft HoloLens 集成，并在真实室内场景中展示了其优势。我们的项目网站为 https://gamma.umd.edu/pace/。