# Text to Video Datasets: AI Datasets Source: https://savrn.com/datasets/tasks/text-to-video Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- SAVRN Model Hub · Datasets by Task # Text to Video Datasets 7 open-weight text to video datasets in the SAVRN Model Hub, with Zhengming, Yiming Hao and H publishing the most. 7Datasets 7Publishers 5Licenses ## Most Downloaded | Dataset | Publisher | License | Monthly downloads | | --- | --- | --- | --- | | [seedance-2-prompts-datasets](https://savrn.com/datasets/seedance-2-prompts-datasets) | MaBiao | cc-by-4.0 | 173.4k | | [h3-fewstep-benchmark-20260920](https://savrn.com/datasets/h3-fewstep-benchmark-20260920) | Phd | Not stated | 4.3k | | [hyperscene_traj](https://savrn.com/datasets/hyperscene-traj) | Yiming Hao | cc-by-4.0 | 744 | | [PAWBench-Results](https://savrn.com/datasets/pawbench-results) | YuandongPu | cc-by-4.0 | 454 | | [anime-ai-training-dataset](https://savrn.com/datasets/anime-ai-training-dataset) | H | mit | — | | [GroOT-MOT17](https://savrn.com/datasets/groot-mot17) | Mohamed Bassat | cc-by-nc-sa-3.0 | — | | [DMAD-H3-data](https://savrn.com/datasets/dmad-h3-data) | Zhengming | other | — | ## Licenses | License | Datasets | Commercial use | | --- | --- | --- | | cc-by-4.0 | 3 | Yes | | other | 1 | Read the license | | not stated | 1 | Not stated | | mit | 1 | Yes | | cc-by-nc-sa-3.0 | 1 | Read the license | ## Who Publishes Them | Publisher | Datasets | | --- | --- | | [Zhengming](https://savrn.com/model-publishers/zhengmingyu) | 1 | | [Yiming Hao](https://savrn.com/model-publishers/ymhao) | 1 | | [H](https://savrn.com/model-publishers/tertrxcuvuvjvj) | 1 | | [Phd](https://savrn.com/model-publishers/hffordata) | 1 | | [MaBiao](https://savrn.com/model-publishers/gokuscraper) | 1 | | [Mohamed Bassat](https://savrn.com/model-publishers/bassatbassat) | 1 | ## All 7 Datasets Dataset · Text to video ### [seedance-2-prompts-datasets](https://savrn.com/datasets/seedance-2-prompts-datasets) [MaBiao](https://savrn.com/model-publishers/gokuscraper) This project is a massive collection of prompts used for Bytedance's Seedance 2.0 and the resulting generated videos. The entire dataset exceeds 50GB and contains 8100+ videos, all structured into a comprehensive dataset. Due to GitHub's limitations with large file storage, the full dataset is hosted on Hugging Face. The Hugging Face repository contains the generated videos (.mp4), cover images (.jpg), and a highly structured.jsonl file that holds all prompt metadata. No login required, lighting-fast response. Launched by GokuOpenLab, seedance-2-prompts-datasets is a prompt data infrastructure project created for developers and researchers. In the current AI ecosystem, prompts are the new… Publicly accessible cc-by-4.0 1K= 3 (24,560 of 46,309 renders); 2. trajectory dedup within a scene: position Hausdorff = 5 instances of at least 0.1% area, c0 >= 0.6 of their area seen later. One clip has one key… Access requested at publisher cc-by-4.0 [View dataset](https://savrn.com/datasets/hyperscene-traj) Dataset · Text to video ### [PAWBench-Results](https://savrn.com/datasets/pawbench-results) [YuandongPu](https://savrn.com/model-publishers/andrew613) This dataset package defines the clean HF repository layout and contains the public metadata needed to run the PAWBench Full31 coverage and Table1 PAW-Cal PAWEval rerun inputs. In the local no-copy handoff, videos are not duplicated under this directory; they are uploaded into sharded videos/bymediaid/ subdirectories from the private upload plan after explicit human approval. - videos/bymediaid/ /.mp4: one physical video per mediaid after human upload from the private upload plan. - manifests/sharedmediaregistry.jsonl: public media identity registry. - manifests/full31pawevalmanifest.jsonl: Full31 logical PAWEval rows. - manifests/table1pawevalrerunrows.jsonl: Table1 PAW-Cal rerun rows.… Access requested at publisher cc-by-4.0 [View dataset](https://savrn.com/datasets/pawbench-results) Dataset · Text to video ### [anime-ai-training-dataset](https://savrn.com/datasets/anime-ai-training-dataset) [H](https://savrn.com/model-publishers/tertrxcuvuvjvj) Curated high-quality anime sakuga animation dataset tailored for AI video model training (AnimateDiff, Wan 2.1, HunyuanVideo, CogVideoX, SVD, LTX-Video, etc.). -: Sword fights, hand-to-hand combat, rapid movement. -: Running, acrobatics, jumps, dynamic perspective transitions. -: Subtle character acting, expressions, hair and cloth physics. -: Fire, water, wind, debris, lightning, energy beams. Download the complete dataset package directly using the link below Publicly accessible mit n<1K [View dataset](https://savrn.com/datasets/anime-ai-training-dataset) Dataset · Text to video ### [DMAD-H3-data](https://savrn.com/datasets/dmad-h3-data) [Zhengming](https://savrn.com/model-publishers/zhengmingyu) The preprocessed training data of the DMAD 4-step students of MiniMax-H3 (text-to-audio-video), from the paper DMAD: Distribution Matching as Adversarial Distillation for Fast Visual Generation. The training code is in train/h3 of the DMAD repository; the released students are at ZhengmingYu/DMAD. Everything here is what train/h3/scripts/preparedata.sh produces from UltraVideo (short.csv and the clipsshort1920 clips) and the MiniMax-H3 base model, so training can start without the ~1 GPU-week of preprocessing. No video or audio is stored: the clips are kept only as MiniMax-H3 VAE latents, the captions as text-encoder outputs (the captions themselves are included as text). 303 GB, 59,818… Access requested at publisher other [View dataset](https://savrn.com/datasets/dmad-h3-data) Dataset · Text to video ### [GroOT-MOT17](https://savrn.com/datasets/groot-mot17) [Mohamed Bassat](https://savrn.com/model-publishers/bassatbassat) A text-to-tracklet retrieval benchmark for MOT17 pedestrians. Each captioned MOT17 track is cropped into its own short mp4. A text query must retrieve the matching track from a gallery of 454 tracks. The layout is the same as bassatbassat/TVPReid, so the same evaluation code runs on both. This is an unofficial derivative. The videos come from MOT17 and the captions come from GroOT. The credit for both belongs to their authors (see below). What this repository adds is listed under Our contribution. All configs share the same 454-track gallery. Only the queries differ. Tracks without a caption of the requested type stay in the gallery as distractors. Read the action numbers with care. GroOT… Publicly accessible cc-by-nc-sa-3.0 [View dataset](https://savrn.com/datasets/groot-mot17) ## Other Tasks - [Text Generation](https://savrn.com/datasets/tasks/text-generation) 142 - [Robotics](https://savrn.com/datasets/tasks/robotics) 123 - [Text Classification](https://savrn.com/datasets/tasks/text-classification) 52 - [Other](https://savrn.com/datasets/tasks/other) 51 - [Text Retrieval](https://savrn.com/datasets/tasks/text-retrieval) 44 - [Question Answering](https://savrn.com/datasets/tasks/question-answering) 40 - [Speech Recognition](https://savrn.com/datasets/tasks/speech-recognition) 35 - [Image to Text](https://savrn.com/datasets/tasks/image-to-text) 30 - [Time Series Forecasting](https://savrn.com/datasets/tasks/time-series-forecasting) 23 - [Tabular Classification](https://savrn.com/datasets/tasks/tabular-classification) 22 - [Image Classification](https://savrn.com/datasets/tasks/image-classification) 18 - [Visual Question Answering](https://savrn.com/datasets/tasks/visual-question-answering) 17 [See all](https://savrn.com/datasets/tasks)