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. Human Evaluation Set and Metrics.We collect an evaluation set from Amazon Mechanical Turk (AMT) that consists of 300 prompts. We asked annotators what they would be interested in generating if there were a T2V system. We filtered out prompts that were incomplete (e.g., “jump into water”), too abstract (e.g., “climate change”), or offensive. We then identified 5 categories (animals, fantasy, people, nature and scenes, food and beverage) and selected prompts for these categories. These prompts were selected without generating any videos for them, and were kept fixed. In addition, we also used the DrawBench prompts from Imagen (Saharia et al., 2022) for human evaluation.We evaluate video quality and text-video faithfulness. For video quality, we show two videos in random order and ask annotators which one is of higher quality. For faithfulness, we additionally show the text and ask annotators which video has a better correspondence with the text (we suggest them to ignore quality issues). In addition, we also conducted human evaluation to compare video motion realism of our interpolation model and FILM (Reda et al., 2022).For each comparison, we use the majority vote from 5 different annotators as the final result. 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: To me, it sounds like an excuse for not collecting the dataset. How difficult is it to collect an open-source text-to-video dataset? What kind of procedure does it contain?
It is hard to collect datasets because a similarly sized (text, video) dataset cannot be easily collected. For human evaluation, they employ some annotators and filtered out according to their criteria. Therefore, they are not making an excuse about not collecting the dataset.