ShareGPT4Video: Improving Video Understanding and Generation with Better Captions

TL;DR

This paper introduces ShareGPT4Video, a comprehensive series including a large dataset, a captioning model, and an LVLM, all designed to enhance video understanding and generation through dense, precise captions and a novel captioning strategy.

Contribution

It presents a new high-quality video captioning dataset, a scalable captioning model, and an LVLM that achieves state-of-the-art performance on video benchmarks, addressing key challenges in temporal and detailed content understanding.

Findings

ShareGPT4Video dataset contains 40K annotated videos with rich captions.

ShareCaptioner-Video efficiently generates high-quality captions for arbitrary videos.

ShareGPT4Video-8B achieves SOTA results on three video benchmarks.

Abstract

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...