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: Automatic Metrics.For UCF-101, we write one template sentence for each class (without generating any video) and fix it for evaluation. We report Frechet Video Distance (FVD) and Inception Score (IS) on 10K samples following (Ho et al., 2022). We generate samples that follow the same class distribution as the training set. For MSR-VTT, we report Frechet Inception Distance (FID) (Parmar et al., 2022) and CLIPSIM (average CLIP similarity between video frames and text) (Wu et al., 2021a), where all 59,794 captions from the test set are used, following (Wu et al., 2021b). 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.

Question: How could this vastness be defined or quantitatively measured?
They employed annotators to make prompts and filtered out them correctly. Evaluation is done about video quality and faithfulness. For each comparison, 5 different annotators are employed. They report FVD and IS on 10K samples and generate samples that follow the same class distribution as the training set. Moreover, for MSR-VTT, FID and CLIPSIM are introduced.