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

翻译：视频字幕生成旨在通过自然语言传达视频中的动态场景，促进对环境中的时空信息的理解。尽管近年来取得了进展，但生成详细且丰富的视频描述仍然是一个重大挑战。在这项工作中，我们引入了Video ChatCaptioner，这是一种用于创建更全面时空视频描述的创新方法。我们的方法使用ChatGPT模型作为控制器，专门设计用于选择帧以提出视频内容驱动的问题。随后，利用一个稳健的算法来回答这些视觉查询。这种问答框架有效揭示了复杂的视频细节，并显示出作为增强视频内容方法的前景。经过多轮对话后，ChatGPT可以根据之前的对话总结出更丰富的视频内容。我们定性证明，我们的Video ChatCaptioner能够生成包含更多视频视觉细节的描述。代码可在https://github.com/Vision-CAIR/ChatCaptioner公开获取。