UGC-VideoCaptioner: An Omni UGC Video Detail Caption Model and New Benchmarks

TL;DR

This paper introduces UGC-VideoCap, a new benchmark and a 3B parameter model for detailed omnimodal captioning of user-generated videos, emphasizing the integration of audio and visual modalities to improve understanding.

Contribution

The paper presents a novel benchmark dataset with balanced audio-visual annotations and a lightweight captioning model trained with a two-stage strategy for improved multimodal video understanding.

Findings

Benchmark includes 1000 TikTok videos with multimodal annotations.

Proposed model achieves competitive performance with limited data.

Two-stage training enhances model adaptation and efficiency.

Abstract

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.