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Live commenting on video, a popular feature of live streaming platforms,enables viewers to engage with the content and share their comments, reactions,opinions, or questions with the streamer or other viewers while watching thevideo or live stream. It presents a challenging testbed for AI agents, whichinvolves the simultaneous understanding of audio-visual multimodal contextsfrom live streams and the ability to interact with human viewers throughdialogue. As existing live streaming-based comments datasets contain limitedcategories and lack a diversity, we create a large-scale audio-visualmultimodal dialogue dataset to facilitate the development of live commentingtechnologies. The data is collected from Twitch, with 11 different categoriesand 575 streamers for a total of 438 hours of video and 3.2 million comments.Moreover, we propose a novel multimodal generation model capable of generatinglive comments that align with the temporal and spatial events within the video,as well as with the ongoing multimodal dialogue context. Our initial resultshave demonstrated the effectiveness of the proposed model, providing a robustfoundation for further research and practical applications in the field of livevideo interaction.

LiveChat: Video Comment Generation from Audio-Visual Multimodal Contexts