Any2Caption:Interpreting Any Condition to Caption for Controllable Video
  Generation
Authors: name: Shengqiong Wu, name: Weicai Ye, name: Jiahao Wang, name: Quande Liu, name: Xintao Wang, name: Pengfei Wan, name: Di Zhang, name: Kun Gai, name: Shuicheng Yan, name: Hao Fei, name: Tat-Seng Chua

To address the bottleneck of accurate user intent interpretation within the
current video generation community, we present Any2Caption, a novel framework
for controllable video generation under any condition. The key idea is to
decouple various condition interpretation steps from the video synthesis step.
By leveraging modern multimodal large language models (MLLMs), Any2Caption
interprets diverse inputs--text, images, videos, and specialized cues such as
region, motion, and camera poses--into dense, structured captions that offer
backbone video generators with better guidance. We also introduce Any2CapIns, a
large-scale dataset with 337K instances and 407K conditions for
any-condition-to-caption instruction tuning. Comprehensive evaluations
demonstrate significant improvements of our system in controllability and video
quality across various aspects of existing video generation models. Project
Page: https://sqwu.top/Any2Cap/