  Advancements in multimodal learning, particularly in video understanding and
generation, require high-quality video-text datasets for improved model
performance. Vript addresses this issue with a meticulously annotated corpus of
12K high-resolution videos, offering detailed, dense, and script-like captions
for over 420K clips. Each clip has a caption of ~145 words, which is over 10x
longer than most video-text datasets. Unlike captions only documenting static
content in previous datasets, we enhance video captioning to video scripting by
documenting not just the content, but also the camera operations, which include
the shot types (medium shot, close-up, etc) and camera movements (panning,
tilting, etc). By utilizing the Vript, we explore three training paradigms of
aligning more text with the video modality rather than clip-caption pairs. This
results in Vriptor, a top-performing video captioning model among open-source
models, comparable to GPT-4V in performance. Vriptor is also a powerful model
capable of end-to-end generation of dense and detailed captions for long
videos. Moreover, we introduce Vript-Hard, a benchmark consisting of three
video understanding tasks that are more challenging than existing benchmarks:
Vript-HAL is the first benchmark evaluating action and object hallucinations in
video LLMs, Vript-RR combines reasoning with retrieval resolving question
ambiguity in long-video QAs, and Vript-ERO is a new task to evaluate the
temporal understanding of events in long videos rather than actions in short
videos in previous works. All code, models, and datasets are available in
https://github.com/mutonix/Vript.
