论文标题
单眼视频的接触和人类动态
Contact and Human Dynamics from Monocular Video
论文作者
论文摘要
现有的深层模型可预测近似准确的视频中的2D和3D运动学姿势，但包含违反物理约束的明显错误，例如脚穿透地面和极端角度倾斜的身体。在本文中，我们提出了一种基于物理学的方法，用于从视频序列中推断出3D人体运动，该方法将初始2D和3D姿势估计作为输入。我们首先通过新的预测网络估算地面接触时间，该网络是在没有手工标记数据的情况下进行训练的。然后，基于物理的轨迹优化基于输入，可以解决物理上可行的运动。我们表明，这一过程产生的动作比纯粹的运动学方法更现实，从而大大改善了运动学和动态合理性的定量测量。我们演示了有关角色动画的方法，并构成了具有复杂接触模式的舞蹈和运动动态运动的估计任务。
Existing deep models predict 2D and 3D kinematic poses from video that are approximately accurate, but contain visible errors that violate physical constraints, such as feet penetrating the ground and bodies leaning at extreme angles. In this paper, we present a physics-based method for inferring 3D human motion from video sequences that takes initial 2D and 3D pose estimates as input. We first estimate ground contact timings with a novel prediction network which is trained without hand-labeled data. A physics-based trajectory optimization then solves for a physically-plausible motion, based on the inputs. We show this process produces motions that are significantly more realistic than those from purely kinematic methods, substantially improving quantitative measures of both kinematic and dynamic plausibility. We demonstrate our method on character animation and pose estimation tasks on dynamic motions of dancing and sports with complex contact patterns.
