从视频中直观物理的抗阻塞学习
Occlusion resistant learning of intuitive physics from videos
摘要：
为了达到人类在复杂任务中的表现，人工系统的一个关键能力是理解物体之间的物理相互作用，并预测未来的结果。这种能力，通常被称为直观的物理，最近受到关注，并提出了几种方法来学习这些物理规则从视频序列。然而，这些方法中的大多数局限于不发生闭塞或仅发生有限闭塞的情况。在这项工作中，我们提出了一个概率公式学习直观的物理在3D场景中具有显着的对象间的闭塞。在我们的配方中，对象的位置被建模为潜变量，使场景的重建。然后，我们提出了一系列的近似，使这个问题易于处理。对象提案使用循环交互网络（在对象空间中建模物理学）和合成渲染器（建模对象投影到像素空间的方式）的组合跨帧链接。我们展示了IntPhys直观物理基准的最新技术水平的显着改进。我们将我们的方法应用于第二个遮挡程度不断增加的数据集，显示它可以真实地预测未来高达30帧的分割掩模。最后，我们还展示了在真实视频中预测对象运动的结果。
英文摘要：
To reach human performance on complex tasks, a key ability for artificial systems is to understand physical interactions between objects, and predict future outcomes of a situation. This ability, often referred to as intuitive physics, has recently received attention and several methods were proposed to learn these physical rules from video sequences. Yet, most of these methods are restricted to the case where no, or only limited, occlusions occur. In this work we propose a probabilistic formulation of learning intuitive physics in 3D scenes with significant inter-object occlusions. In our formulation, object positions are modeled as latent variables enabling the reconstruction of the scene. We then propose a series of approximations that make this problem tractable. Object proposals are linked across frames using a combination of a recurrent interaction network, modeling the physics in object space, and a compositional renderer, modeling the way in which objects project onto pixel space. We demonstrate significant improvements over state-of-the-art in the intuitive physics benchmark of IntPhys. We apply our method to a second dataset with increasing levels of occlusions, showing it realistically predicts segmentation masks up to 30 frames in the future. Finally, we also show results on predicting motion of objects in real videos.
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