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Context: Datasets.To train the image models, we use a 2.3B subset of the dataset from (Schuhmann et al., ) where the text is English. We filter out sample pairs with NSFW images 333We used this model: https://github.com/GantMan/nsfw_model, toxic words in the text, or images with a watermark probability larger than 0.5.We use WebVid-10M (Bain et al., 2021) and a 10M subset from HD-VILA-100M (Xue et al., 2022) 444These 100M clips are sourced from 3.1M videos. We randomly downloaded 3 clips per video to form our HD-VILA-10M subset. to train our video generation models.Note that only the videos (no aligned text) are used. The decoder \operatorname{D}^{t} and the interpolation model is trained on WebVid-10M. \operatorname{SR}_{l}^{t} is trained on both WebVid-10M and HD-VILA-10M.While prior work (Hong et al., 2022; Ho et al., 2022) have collected private text-video pairs for T2V generation, we use only public datasets (and no paired text for videos). We conduct automatic evaluation on UCF-101 (Soomro et al., 2012) and MSR-VTT (Xu et al., 2016) in a zero-shot setting. 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: What kind of text prompt does it contain? What were the criteria to set these prompts?
For a more thorough evaluation than existing literature in T2V, the authors collect an evaluation set from Amazon Mechanical Turk (AMT) that consists of 300 prompts and filtered out prompts that were incomplete, too abstract, or offensive and then identified 5 categories (animals, fantasy, people, nature and scenes, food and beverage) and selected prompts for these categories. It is used for zero-shot T2V human evaluation.