UGC-VideoCaptioner: An Omni UGC Video Detail Caption Model and New
  Benchmarks
Authors: name: Peiran Wu, name: Yunze Liu, name: Zhengdong Zhu, name: Enmin Zhou, name: Shawn Shen

Real-world user-generated videos, especially on platforms like TikTok, often
feature rich and intertwined audio visual content. However, existing video
captioning benchmarks and models remain predominantly visual centric,
overlooking the crucial role of audio in conveying scene dynamics, speaker
intent, and narrative context. This lack of omni datasets and lightweight,
capable models hampers progress in fine grained, multimodal video
understanding. To address these challenges, we introduce UGC-VideoCap, a new
benchmark and model framework specifically designed for detailed omnimodal
captioning of short form user-generated videos. Unlike prior datasets,
UGC-VideoCap emphasizes balanced integration of audio and visual modalities,
featuring 1000 TikTok videos annotated through a structured three stage
human-in-the-loop pipeline covering audio only, visual only, and joint audio
visual semantics. The benchmark also includes 4000 carefully crafted QA pairs
probing both unimodal and cross modal understanding. Alongside the dataset, we
propose UGC-VideoCaptioner(3B), a 3B parameter captioning model distilled from
Gemini 2.5 Flash. Using a novel two-stage training strategy supervised fine
tuning followed by Group Relative Policy Optimization (GRPO), our approach
enables efficient adaptation from limited data while maintaining competitive
performance. Together, our benchmark and model offer a high-quality foundation
and a data-efficient solution for advancing omnimodal video captioning in
unconstrained real-world UGC settings.