Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

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
Many phenomena, both natural and artificial, are naturally characterized as transformations -the most salient information about them is contained in the start and end states, given which it is possible to fill in intermediate states from prior experience.
For example, ending up in San Francisco after starting in Oakland entails getting into a car and crossing the Bay Bridge.
Similarly, to an expert engineer observing a bridge, the task of reverse-engineering how it was built is well-defined and tractable.
In contrast, consider the task of predicting forward in time, having observed only the steel and concrete that went into making the bridge.
Such forward prediction tasks are severely underconstrained, leading to high uncertainties that compound with time, making it impossible to make meaningful predictions after only a few stages of iterative forward prediction (see Fig. 1 ).
This is aggravated in highdimensional settings such as forward video prediction, which despite being the most widely studied setting for video synthesis, struggles to produce coherent video longer than a few seconds.
We propose to condition video synthesis instead on the substantially more informative context of the start and the goal frame.
We term such models goal-conditioned predictors (GCP).
Much like the engineer observing the bridge, GCPs treat long videos as start-goal transformations and reverseengineer the full video, conditioned on the first and final frames.
The simplest instantiation of GCPs modifies existing forward prediction approaches to also observe the final frame.
More broadly, once we consider conditioning on the goal frame, we can devise new types of GCP models that more efficiently leverage the hierarchical structure present in real-world event sequences ( Fig. 1, right) .
Just as coarse-to-fine image synthesis (Karras et al., 2017) generates a high-resolution image by iteratively adding details to a low-resolution image, we can synthesize a temporally downsampled video in the form of sequences of keyframes, and fill it in iteratively.
We propose to
In our experiments, all GCP variants successfully generate longer and higher-quality video than has been demonstrated with standard auto-regressive video prediction models, which only utilize the starting frames for context.
Furthermore, we show that tree-structured GCPs are more parallelizable than auto-regressive models, leading to very fast training and inference.
We show that we can train tree-structured GCPs on videos consisting of thousands of frames.
We also study the applications of GCPs, demonstrating that they can be utilized to enable prediction-based control in simulated imitation learning scenarios.
In these settings, the GCP models can be trained without access to demonstrator actions, and can synthesize visual plans directly from start and goal images, which can then be tracked using an inverse model.
We presented goal-conditioned predictors (GCPs) -predictive models that generate video sequences between a given start and goal frame.
GCPs must learn to understand the mechanics of the environment that they are trained in, in order to accurately predict the intermediate events that must take place in order to bring about the goal images from the start images.
GCP models not only allow for substantially more accurate video prediction than conventional models that are conditioned only on the beginning context, but also allow for novel model architectures.
Specifically, we explore how, in addition to more conventional auto-regressive GCPs, we can devise tree-structured GCP models that predict video sequences hierarchically, starting with the coarsest level subgoals and recursively subdividing until a full sequence is produced.
Our experimental results show that GCPs can make more accurate predictions.
We also demonstrate that they can be utilized in an imitation learning scenario, where they can learn behaviors from video demonstrations without example actions.
Imitation from observations, without actions, is applicable in a wide range of realistic scenarios.
For example, a robot could learn the mechanics of cooking from watching videos on YouTube (Damen et al., 2018) , and then use this model to learn how to cook on its own.
We hope that the imitation framework presented in our work can be a step in towards effectively leveraging such data for robotic control.
A DATA PROCESSING For the Human 3.6 dataset, we downsample the original videos to 64 by 64 resolution.
We obtain videos of length of roughly 800 to 1600 frames, which we randomly crop in time to 500-frame sequences.
We split the Human 3.6 into training, validation and test set by correspondingly 95%, 5% and 5% of the data.
On the TAP dataset, we use 48949 videos for training, 200 for validation and 200 for testing.
We propose a new class of visual generative models: goal-conditioned predictors. We show experimentally that conditioning on the goal allows to reduce uncertainty and produce predictions over much longer horizons.