  Video captioning aims to convey dynamic scenes from videos using natural
language, facilitating the understanding of spatiotemporal information within
our environment. Although there have been recent advances, generating detailed
and enriched video descriptions continues to be a substantial challenge. In
this work, we introduce Video ChatCaptioner, an innovative approach for
creating more comprehensive spatiotemporal video descriptions. Our method
employs a ChatGPT model as a controller, specifically designed to select frames
for posing video content-driven questions. Subsequently, a robust algorithm is
utilized to answer these visual queries. This question-answer framework
effectively uncovers intricate video details and shows promise as a method for
enhancing video content. Following multiple conversational rounds, ChatGPT can
summarize enriched video content based on previous conversations. We
qualitatively demonstrate that our Video ChatCaptioner can generate captions
containing more visual details about the videos. The code is publicly available
at https://github.com/Vision-CAIR/ChatCaptioner
