Dataset · Video text to text
MVP Lab
Training data for the LLaVA-OneVision-2 multimodal model family. The release contains large-scale video data at several duration ranges, video captions and source mappings, and spatial-reasoning data used for mid-training. The dataset is split across two Hugging Face repositories because of its size: If you need the long-video data, download the video shards from Part 2 and use the corresponding captions and mapping files from Part 1. The directory names in Part 2 are upload prefixes. Their duration mapping is: The mapping above was verified from the sample paths stored inside the WebDataset archives. For the ~180-second split: 1. Download video shards from tom/ in Part 2. 2. Read captions…
Publicly accessible
apache-2.0
10M<n<100M
Data cleaning, stay tuned! Please refer to https://egolife-ai.github.io/ first for general info. Checkout the paper EgoLife (https://arxiv.org/abs/2503.03803) for more information. Code: https://github.com/egolife-ai/EgoLife
Publicly accessible
mit
Derived from HuggingFaceFV/finevideo (https://huggingface.co/datasets/HuggingFaceFV/finevideo). Videos are Creative Commons; original licenses apply, including attribution where required. Provenance for every clip is in its.json (provenance, sourceid). must update to the latest usable version (use reshardvideo.py purge). Hosting or sharing requires including the FineVideo Terms of Use and requiring users to agree. If this is hosted publicly it must be gated, with these terms shown and accepted by every user. Modifications: cut into short clips, resized, re-encoded (H.264), audio removed. FineVideo dataset is a collection of over 43.000 YouTube videos. We ask that you read and acknowledge…
Access requested at publisher
cc-by-4.0
Derived from nkp37/OpenVid-1M (https://huggingface.co/datasets/nkp37/OpenVid-1M), released under CC-BY-4.0 for research and non-commercial purposes. Videos were collected by its authors from other publicly available datasets, whose own terms may also apply. Attribution: "OpenVid-1M: A Large-Scale High-Quality Dataset for Text-to-video Generation" (ICLR 2025), NJU-PCALab. License: https://creativecommons.org/licenses/by/4.0/ Modifications: cut/cropped into short clips, resized, re-encoded (H.264), audio removed.
Publicly accessible
cc-by-4.0
InteractionBench: A Real-Time Interaction Benchmark for Streaming Video Systems A real-time interaction system watches a video stream and decides, at every moment, whether to speak or to stay silent. Each item of InteractionBench pairs a video with a question or a standing instruction and with timed ground-truth responses: what should be said, and the earliest moment at which it should be said. Items that require silence for the whole stream are included. This repository contains the videos, the questions and the ground truth. The evaluation code, the protocols and the baseline runners are in the is the collection directory of the video. An item is identified by #, for example…
Publicly accessible
1K<n<10K
