You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: The Internet has fueled collecting billions of (alt-text, image) pairs from HTML pages (Schuhmann et al., 2022), enabling the recent breakthroughs in Text-to-Image (T2I) modeling. However, replicating this success for videos is limited since a similarly sized (text, video) dataset cannot be easily collected.It would be wasteful to train Text-to-Video (T2V) models from scratchwhen there already exist models that can generate images.Moreover, unsupervised learning enables networks to learn from orders of magnitude more data. This large quantity of data is important to learn representations of more subtle, less common concepts in the world. Unsupervised learning has long had great success in advancing the field of natural language processing (NLP) (Liu et al., 2019a; Brown et al., 2020). Models pre-trained this way yield considerably higher performance than when solely trained in a supervised manner. Clearly, text describing images does not capture the entirety of phenomena observed in videos. That said, one can often infer actions and events from static images (e.g. a woman drinking coffee, or an elephant kicking a football) as done in image-based action recognition systems (Girish et al., 2020). Moreover, even without text descriptions, unsupervised videos are sufficient to learn how different entities in the world move and interact (e.g. the motion of waves at the beach, or of an elephant’s trunk). As a result, a model that has only seen text describing images is surprisingly effective at generating short videos, as demonstrated by our temporal diffusion-based method. Make-A-Video sets the new state-of-the-art in T2V generation. Learning from the world around us is one of the greatest strengths of human intelligence. Just as we quickly learn to recognize people, places, things, and actions through observation, generative systems will be more creative and useful if they can mimic the way humans learn. Learning world dynamics from orders of magnitude more videos using unsupervised learning helps researchers break away from the reliance on labeled data. The presented work has shown how labeled images combined effectively with unlabeled video footage can achieve that. As with all large-scale models trained on data from the web, our models have learnt and likely exaggerated social biases, including harmful ones. Our T2I generation model was trained on data that removed NSFW content and toxic words. All our data (image as well as videos) is publicly available, adding a layer of transparency to our models, and making it possible for the community to reproduce our work. Our main contributions are:•We present Make-A-Video – an effective method that extends a diffusion-based T2I model to T2V through a spatiotemporally factorized diffusion model.•We leverage joint text-image priors to bypass the need for paired text-video data, which in turn allows us to potentially scale to larger quantities of video data.•We present super-resolution strategies in space and time that, for the first time, generate high-definition, high frame-rate videos given a user-provided textual input.•We evaluate Make-A-Video against existing T2V systems and present: (a) State-of-the-art results in quantitative as well as qualitative measures, and (b) A more thorough evaluation than existing literature in T2V. We also collect a test set of 300 prompts for zero-shot T2V human evaluation which we plan to release.

Question: What are the reasons for such results? Why does it excel in such cases?
A model that has only seen text describing images is surprisingly effective at generating short videos, as demonstrated by our temporal diffusion-based method. A diffusion-based T2I model to T2V through a spatiotemporally factorized diffusion model. They leverage joint text-image priors to bypass the need for paired text-video data, which in turn allows us to potentially scale to larger quantities of video data.