PACE：在密集杂乱环境中基于数据的虚拟智能体交互
PACE: Data-Driven Virtual Agent Interaction in Dense and Cluttered Environments
AI总结：
本研究提出PACE方法，通过优化动作帧高自由度姿态与新型损失函数，让虚拟智能体适配密集杂乱3D场景，在感知偏好和物理合理性指标上优于现有方法，还可集成于Microsoft HoloLens用于真实场景。
摘要：
我们提出了PACE，这是一种用于修改动作捕捉虚拟智能体以在密集、杂乱的3D场景中交互和移动的新方法。我们的方法根据需要调整虚拟智能体的给定动作序列，以适应环境中的障碍物和物体。我们首先选取动作序列中对建模与场景交互最重要的单帧，将其与相关的场景几何、障碍物和语义信息配对，使智能体动作中的交互与场景的可供性（例如站在地板上或坐在椅子上）相匹配。随后，我们通过直接改变动作中每帧的高自由度（high-DOF）姿态来优化人体动作，以更好地适配场景独特的几何约束。我们的公式采用了新颖的损失函数，可维持真实的动作流和自然的视觉效果。我们将该方法与先前的动作生成技术进行了比较，并通过感知研究和物理合理性指标凸显了方法的优势。人类评分者更偏好我们的方法而非现有方法。具体而言，与使用现有动作的前沿方法相比，我们的方法在57.1%的情况下更受青睐；与前沿的动作合成方法相比，这一比例为81.0%。此外，我们的方法在已建立的物理合理性和交互指标上表现显著更优。具体来说，我们在无碰撞指标上比竞争方法高出1.2%以上，在接触指标上高出18%以上。我们已将该交互系统与Microsoft HoloLens集成，并展示了其在真实室内场景中的优势。我们的项目网站为https://gamma.umd.edu/pace/。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
