# DanceCaps: Pseudo-Captioning for Dance Videos Using Large Language Models
## 4 Experiments
### User Study  
To evaluate the significance of the generated tags and captions, as well as to verify the quality of the dataset, a user study was conducted. Since no prior research existed on dance captioning, there were no benchmark datasets available. Thus, we defined evaluation criteria based on studies from audio and music captioning and conducted a qualitative assessment. The study involved a total of 34 participants, including 6 experts and 28 non-experts, who evaluated the captions at their convenience after receiving adequate instructions. The group consisted of 11 men and 23 women. Participants reviewed the videos and captions and answered questions through a web-based survey. Evaluation criteria included whether the captions appeared to be written by a professional dancer, whether they aligned with the video content, and how well the caption details matched the video, all rated on a five-point scale. Additionally, participants evaluated the 'dance summary caption' for how comprehensively it covered essential content, the 'time series motion caption' for how accurately it described the movements, and the'multimodal caption' for how well it integrated multiple components, all using the same five-point scale. Furthermore, frame extraction positions and caption type preferences were assessed. For frame extraction positions, captions generated at beat, half-beat, and quarter-beat intervals were compared to determine which best described the video. Finally, participants were provided with all three caption styles for a single video and were asked to select their preferred caption style. The results are summarized in Tables 4-6. First, all three types of dance captions received high ratings in terms of appearing as if they were written by dance professionals. This indicates that, despite being automatically generated, the captions accurately reflected the content of the videos. For the first type, the 'dance summary caption', the evaluations for captions from both the AIST dance dataset and the in-the-wild video dataset were similar. Although the differences between the two datasets were minimal, this caption type tended to score lower in terms of the amount of information conveyed compared to the other two types. Participants noted that while the dance summary captions effectively described and summarized the dances, they lacked the level of detail present in the other caption types. Additionally, the alignment between the video and the caption was rated relatively lower, likely due to the omission of key movement details, as well as tags related to music and the environment, during the summarization process, leading to a lower information density. The second type, the 'time series motion caption', focused on describing the dance movements chronologically and included the most information, being the longest caption type. It was rated higher in terms of how well it captured the dance content compared to the other two types. Furthermore, regarding the alignment between the video and the caption, the 'time series motion caption' received the highest scores. These two metrics suggest that participants prioritized the detailed description of movements when assessing the captions. Despite its longer length, the 'time series motion caption' was also seen as efficient in conveying relevant information without including unnecessary details. Lastly, although the'multimodal caption' incorporated several additional tags, it did not score higher than the 'time series motion caption'. This implies that when reading dance captions, participants prioritize the structure and composition of the movements over information about the music or environment. However, the'multimodal caption' was rated higher than the 'dance summary caption' in terms of the amount of information and the overall structure. Although it is shorter than the 'time series motion caption', the'multimodal caption' compresses more information and effectively integrates various elements into a cohesive and consistent format.