ARC-Hunyuan-Video-7B: Structured Video Comprehension of Real-World
  Shorts
Authors: name: Yuying Ge, name: Yixiao Ge, name: Chen Li, name: Teng Wang, name: Junfu Pu, name: Yizhuo Li, name: Lu Qiu, name: Jin Ma, name: Lisheng Duan, name: Xinyu Zuo, name: Jinwen Luo, name: Weibo Gu, name: Zexuan Li, name: Xiaojing Zhang, name: Yangyu Tao, name: Han Hu, name: Di Wang, name: Ying Shan

Real-world user-generated short videos, especially those distributed on
platforms such as WeChat Channel and TikTok, dominate the mobile internet.
However, current large multimodal models lack essential temporally-structured,
detailed, and in-depth video comprehension capabilities, which are the
cornerstone of effective video search and recommendation, as well as emerging
video applications. Understanding real-world shorts is actually challenging due
to their complex visual elements, high information density in both visuals and
audio, and fast pacing that focuses on emotional expression and viewpoint
delivery. This requires advanced reasoning to effectively integrate multimodal
information, including visual, audio, and text. In this work, we introduce
ARC-Hunyuan-Video, a multimodal model that processes visual, audio, and textual
signals from raw video inputs end-to-end for structured comprehension. The
model is capable of multi-granularity timestamped video captioning and
summarization, open-ended video question answering, temporal video grounding,
and video reasoning. Leveraging high-quality data from an automated annotation
pipeline, our compact 7B-parameter model is trained through a comprehensive
regimen: pre-training, instruction fine-tuning, cold start, reinforcement
learning (RL) post-training, and final instruction fine-tuning. Quantitative
evaluations on our introduced benchmark ShortVid-Bench and qualitative
comparisons demonstrate its strong performance in real-world video
comprehension, and it supports zero-shot or fine-tuning with a few samples for
diverse downstream applications. The real-world production deployment of our
model has yielded tangible and measurable improvements in user engagement and
satisfaction, a success supported by its remarkable efficiency, with stress
tests indicating an inference time of just 10 seconds for a one-minute video on
H20 GPU.