Perception Updating Networks: On architectural constraints for interpretable video generative models
We investigate a neural network architecture and statistical framework that models frames in videos using principles inspired by computer graphics pipelines. The proposed model explicitly represents *sprites* or its percepts inferred from maximum likelihood of the scene and infers its movement independently of its content. We impose architectural constraints that forces resulting architecture to behave as a recurrent what-where prediction network.
This paper presents an approach to modeling videos based on a decomposition into a background + 2d sprites with a latent hidden state. The exposition is OK, and I think the approach is sensible, but the main issue with this paper is that it is lacking experiments on non-synthetic datasets. As such, while I find the graphics inspired questions the paper is investigating interesting, I don*t think it is clear that this work introduces useful machinery for modeling more general videos. I think this paper is more appropriate as a workshop contribution in its current form.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Perception Updating Networks: On architectural constraints for interpretable video generative models
We investigate a neural network architecture and statistical framework that models frames in videos using principles inspired by computer graphics pipelines. The proposed model explicitly represents *sprites* or its percepts inferred from maximum likelihood of the scene and infers its movement independently of its content. We impose architectural constraints that forces resulting architecture to behave as a recurrent what-where prediction network.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This paper presents an approach to modeling videos based on a decomposition into a background + 2d sprites with a latent hidden state. The exposition is OK, and I think the approach is sensible, but the main issue with this paper is that it is lacking experiments on non-synthetic datasets. As such, while I find the graphics inspired questions the paper is investigating interesting, I don*t think it is clear that this work introduces useful machinery for modeling more general videos. I think this paper is more appropriate as a workshop contribution in its current form.
assistant
This paper presents an approach to modeling videos based on a decomposition into a background + 2d sprites with a latent hidden state. The exposition is OK, and I think the approach is sensible, but the main issue with this paper is that it is lacking experiments on non-synthetic datasets. As such, while I find the graphics inspired questions the paper is investigating interesting, I don*t think it is clear that this work introduces useful machinery for modeling more general videos. I think this paper is more appropriate as a workshop contribution in its current form.