We present the ShareGPT4Video series, aiming to facilitate the video
understanding of large video-language models (LVLMs) and the video generation
of text-to-video models (T2VMs) via dense and precise captions. The series
comprises: 1) ShareGPT4Video, 40K GPT4V annotated dense captions of videos with
various lengths and sources, developed through carefully designed data
filtering and annotating strategy. 2) ShareCaptioner-Video, an efficient and
capable captioning model for arbitrary videos, with 4.8M high-quality aesthetic
videos annotated by it. 3) ShareGPT4Video-8B, a simple yet superb LVLM that
reached SOTA performance on three advancing video benchmarks. To achieve this,
taking aside the non-scalable costly human annotators, we find using GPT4V to
caption video with a naive multi-frame or frame-concatenation input strategy
leads to less detailed and sometimes temporal-confused results. We argue the
challenge of designing a high-quality video captioning strategy lies in three
aspects: 1) Inter-frame precise temporal change understanding. 2) Intra-frame
detailed content description. 3) Frame-number scalability for arbitrary-length
videos. To this end, we meticulously designed a differential video captioning
strategy, which is stable, scalable, and efficient for generating captions for
videos with arbitrary resolution, aspect ratios, and length. Based on it, we
construct ShareGPT4Video, which contains 40K high-quality videos spanning a
wide range of categories, and the resulting captions encompass rich world
knowledge, object attributes, camera movements, and crucially, detailed and
precise temporal descriptions of events. Based on ShareGPT4Video, we further
develop ShareCaptioner-Video, a superior captioner capable of efficiently
generating high-quality captions for arbitrary videos...

通过稠密和精确的字幕，在大视频 - 语言模型（LVLMs）的视频理解和文本 - 视频模型（T2VMs）的视频生成方面，我们提出了 ShareGPT4Video 系列，该系列包括 40K GPT4V 标注的各种长度和来源的视频稠密字幕，通过精心设计的数据过滤和注释策略进行开发，以及有效的任意视频字幕模型 ShareCaptioner-Video 和卓越的 LVLM ShareGPT4Video-8B。