Paper:
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).

Review:
This paper proposes a method to predict future trajectories by modeling partial and full occlusions. Although it is well-written and the topic sounds interesting, I failed to catch why this approach is required for this setting. So, to strengthen the message of this paper, I listed a couple of suggestions and comments below (from the most important to the least important):
1. It is a bit hard to catch how this model handles "diversity." Specifically, when predicting the futures, it should be able to generate stochastic outputs. However, I failed to find how diverse the output of the model is. If the output is not that stochastic, then it would be tough to believe that the model can "predict" the future; instead, it may "extrapolate" the current condition only. To reassure such concerns, I recommend reporting how diverse your output is. (One easy way is to report the variance of the predicted center mass values between multiple samples while reporting the l2 distance.)
2. For the future prediction task, it would be much better if it is compared with various state-of-the-art future prediction approaches [1, 2, 3, 4, 5, 6]. For some of the models, it could not be able to compare directly with this approach (e.g., lack of 'center of mass' information). However, it would be still okay once it is compared with other state-of-the-art results without feeding some 3D information (e.g., provide projected 2D video as an input). By doing so, I believe the readers can easily catch (1) why it is better to predict physical interaction in 3D space (instead of directly predicting from a 2D space), and (2) also why predicting occlusion is essential in this problem setting.
3. Minor comments:
(a) It is a bit hard to catch how the author computes the "aggregate pixel reconstruction error" in Table S1. I recommend adding an equation number there to make it clear.
(b) There are a couple of missing references: the last sentence on page 4, the first paragraph in Supplementary, the last sentence in Supplementary page 3, etc.
(c) \citep is often misused. Please replace some inappropriate \citep with \citet.
(d) Please check the format of the reference, as well; currently, it has various styles even for the same source/conference.

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[Some comments based on the authors' rebuttal]
I thank the authors for their thorough comments and detailed explanations for each question. I carefully read the whole (not just my part), but it didn't change my mind; it would be much better if the claim comes with a more directly comparable result.
Some additional comments:
Q1-comment) I think the limitation of "learning to extrapolate"-style video prediction approach is partially presented in Reviewer #2's claim as well. Therefore, in this context, I recommend the author to show a better result to reassure the reader's concern. 
Q2-comment) I at least strongly recommend to add more experiments with other baselines, rather than relying mainly on the original model of the dataset. Although the input condition of a model could be different, I at least do believe that it will help the readers to catch the benefit of your setting more clearly.
I hope this review phase would make your paper more powerful. 

[1] Liang et al., Dual Motion GAN for Future-Flow Embedded Video Prediction, in ICCV, 2017
[2] Denton and Fergus, Stochastic Video Generation with a Learned Prior, in ICML, 2018
[3] Wichers et al., Hierarchical Long-term Video Prediction without Supervision, in ICML, 2018
[4] Wang et al., Video-to-Video Synthesis, in NeurIPS, 2018
[5] Heish et al., Learning to Decompose and Disentangle Representations for Video Prediction, in NeurIPS, 2018
[6] Minderer et al., Unsupervised Learning of Object Structure and Dynamics from Videos, in NeurIPS, 2019