ARC-Hunyuan-Video-7B: Structured Video Comprehension of Real-World
  Shorts
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.
68882c7eaf872d625c10c5c0
Yuying Ge
68882c7eaf872d625c10c5c1
Yixiao Ge
68882c7eaf872d625c10c5c2
Chen Li
68882c7eaf872d625c10c5c3
Teng Wang
claimed_verified
2025-08-28T08:57:07.471Z
63a9414e32ed73936ec0a0c8
/avatars/f181e1bb480502c2680be5296a036bdd.svg
wybertwang
user
wybertwang
68882c7eaf872d625c10c5c4
Junfu Pu
68882c7eaf872d625c10c5c5
Yizhuo Li
68882c7eaf872d625c10c5c6
Lu Qiu
68882c7eaf872d625c10c5c7
Jin Ma
68882c7eaf872d625c10c5c8
Lisheng Duan
68882c7eaf872d625c10c5c9
Xinyu Zuo
68882c7eaf872d625c10c5ca
Jinwen Luo
68882c7eaf872d625c10c5cb
Weibo Gu
68882c7eaf872d625c10c5cc
Zexuan Li
68882c7eaf872d625c10c5cd
Xiaojing Zhang
68882c7eaf872d625c10c5ce
Yangyu Tao
68882c7eaf872d625c10c5cf
Han Hu
68882c7eaf872d625c10c5d0
Di Wang
68882c7eaf872d625c10c5d1
Ying Shan
ARC-Hunyuan-Video-7B: Structured Video Comprehension of Real-World
  Shorts
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.
ARC-Hunyuan-Video, a multimodal model, processes raw video inputs for structured comprehension, supporting captioning, summarization, question answering, grounding, and reasoning with high efficiency.
multimodal model timestamped video captioning video summarization open-ended video question answering temporal video grounding video reasoning pre-training instruction fine-tuning cold start reinforcement learning ShortVid-Bench zero-shot fine-tuning user engagement satisfaction inference time H20 GPU