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，从而促进对环境中时空信息的理解。尽管近期取得了进展，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，我们提出了Video ChatCaptioner，一种用于创建更全面的时空视频描述的创新方法。我们的方法采用ChatGPT模型作为控制器，专门设计用于选择视频帧以提出基于视频内容的问题。随后，利用鲁棒算法来回答这些视觉查询。这种问答框架有效地揭示了复杂的视频细节，并展现出作为提升视频内容方法的潜力。经过多轮对话后，ChatGPT能够基于先前的对话总结出丰富的视频内容。我们定性地证明，我们的Video ChatCaptioner可以生成包含更多视频视觉细节的字幕。代码已在https://github.com/Vision-CAIR/ChatCaptioner公开。