Many processes can be concisely represented as a sequence of events leading from a starting state to an end state. Given raw ingredients, and a finished cake, an experienced chef can surmise the recipe. Building upon this intuition, we propose a new class of visual generative models: goal-conditioned predictors (GCP). Prior work on video generation largely focuses on prediction models that only observe frames from the beginning of the video. GCP instead treats videos as start-goal transformations, making video generation easier by conditioning on the more informative context provided by the first and final frames. Not only do existing forward prediction approaches synthesize better and longer videos when modified to become goal-conditioned, but GCP models can also utilize structures that are not linear in time, to accomplish hierarchical prediction. To this end, we study both auto-regressive GCP models and novel tree-structured GCP models that generate frames recursively, splitting the video iteratively into finer and finer segments delineated by subgoals. In experiments across simulated and real datasets, our GCP methods generate high-quality sequences over long horizons. Tree-structured GCPs are also substantially easier to parallelize than auto-regressive GCPs, making training and inference very efficient, and allowing the model to train on sequences that are thousands of frames in length. Finally, we demonstrate the utility of GCP approaches for imitation learning in the setting without access to expert actions. Videos are on the supplementary website: https: //sites.google.com/view/video-gcp
This paper reformulates video prediction problem by conditioning the prediction on the start and end (goal) frame. This essentially changes the problem from extrapolation to interpolation which results in higher quality predictions. 
The motivation behind the paper is not clear. First of all, the previous work in video predicted is typically formulated as "conditioned frame prediction" where the prediction of the next frame is conditioned on "a set of context frames" and there is no reason why this set cannot contain the goal frame. Their implementation, however, is motivated by their application and therefore these models are usually only conditioned on the start frames. Unfortunately, besides the reverse planning in imitation learning, the authors did not provide a suite of applications where such a model can be useful. Hence, I think the authors should answer these two questions to clear up the motivation:
1. Why conditioning on the goal frame is interesting? It specifically helps to provide more concrete details than getting from Oakland to San Fransico.
2. Where the current conditional models suffer by conditioning on the goal image?
More experiments are required to support the claims of the paper as well. 
Given my point regarding context frames, a more fair experiment would be to compare the proposed method with them when they are conditioned on the goal frame as well. This explicitly has been avoided in 5.1.
 The used metrics are not a good evaluation metric for frame prediction as they both do not give us an objective evaluation in the sense of the semantic quality of predicted frames. The authors should present additional quantitative evaluation to show that the predicted frames contain useful semantic information. FVD and Inception score come to my mind as good candidates. 
On quality of writing, the paper is well written but it can use a figure that demonstrates proposed architecture. The authors provided the code which is always a plus. 
In conclusion, I believe the impact of the paper, in the current form, is marginal at best and for sure does not meet the requirements for a prestigious conference such as ICLR. However, a more clear motivation, a  concrete set of goals and claims, as well as more comprehensive experiments,  can push the quality above the bar.