title: Occlusion  resistant  learning  of  intuitive physics from videos
abstract: 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 these methods are
  restricted to the case where no occlusions occur, narrowing the potential areas
  of application. The main contribution of this paper is a method combining
  a predictor of object dynamics and a neural renderer efficiently predicting
  future trajectories and explicitly modelling partial and full occlusions among
  objects. We present a training procedure enabling learning intuitive physics
  directly from the input videos containing segmentation masks of objects and
  their depth. Our results show that our model learns object dynamics despite
  significant inter-object occlusions, and realistically predicts segmentation
  masks up to 30 frames in the future. We study model performance for
  increasing levels of occlusions, and compare results to previous work on
  the tasks of future prediction and object following. We also show results
  on predicting motion of objects in real videos and demonstrate significant
  improvements over state-of-the-art on the object permanence task in the
  intuitive physics benchmark of Riochet et al. (2018).
authors: Ronan Riochet Josef Sivic Ivan Laptev Emmanuel Dupoux
hIndex: Josef Sivic 69