| title | Behind Video Generation: Observations and Insights on Multimodal Understanding from Kling |
|---|---|
| authors | Kling Team, Dong Chen, Jialu Chen, Xinlong Chen, Yifan Dai, Haotian Dong, Binzong Geng, Yushuo Guan, Minghui Guo, Guanlin He, Guanxiao He, Jingyun Hua, Yanwei Huang, Yiyan Ji, Xueping Jia, Yizhuo Jia, Bozhou Li, Jiaze Li, Min Li, Shihao Li, Shujie Li, Shengyi Liao, Weihong Lin, Shiyuan Liu, Xinyu Liu, Siyang Mao, Xiaoru Qu, Dianmo Sheng, Yang Shi, Jiafu Tang, Longxiang Tang, Yuqi Tang, Chengzhuo Tong, Kuo Wang, Qixun Wang, Rui Wang, Tianduo Wang, Yuran Wang, Zekun Wang, Zihao Wang, Boyang Xia, Yifan Xu, Liu Yang, Zhenlong Yuan, Bohan Zeng, Mujin Zhang, Yuanxing Zhang, Zhicheng Zhang, Zhuoran Zhang, Yuanhong Zheng, Kun Gai, Chengru Song, Pengfei Wan |
| date | 2026-11-03 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2611.04916 |
| affiliation | KlingAI Research |
| upvotes | 52 (Hugging Face Daily Papers, 2026-11-05) |
| links | abs pdf hf |
| cite | |
| abstract | Video generation has progressed from isolated clips to coherent short-form audiovisual sequences, and the bottleneck is shifting from rendering to semantic control. This report summarizes the observations and insights on multimodal understanding gained from successive generations of Kling. We argue that generation quality is the projection of multimodal understanding through the entire data--conditioning--alignment--evaluation chain, and we examine this chain module by module. Kling-MLLM, a shared multimodal foundation model that unifies image, video, audio, synchronized audio--video, and interleaved inputs, is specialized for each downstream module. Because a DiT is trained on far fewer training samples than a language model, we treat captioning as a semantic contract that maximizes description density, and extend it to reference-grounded captioning that makes reference bindings explicit. Prompt enhancement translates user intent into this caption distribution: basic PE is derived from captions of compelling videos and refined with text-level and generator-in-the-loop reinforcement learning, while agentic PE refines the story and retrieves compelling examples. We show that a reliable text encoder comes from perceptual training rather than from stronger language ability, and that candidates can be screened from text alone before end-to-end training. Reward modeling pairs a general reward model for explainable, localized critiques with a decision reward model for fast multidimensional scoring, treating perceptual bandwidth as a first-class design parameter. Finally, we review task-specific automatic evaluation, focusing on protocols that expose scoring shortcuts and help trace failures to their upstream source. We hope these lessons offer practical guidance for building the understanding stack behind controllable video generation. |
bibtex
@misc{team2026behind,
title={Behind Video Generation: Observations and Insights on Multimodal Understanding from Kling},
author={Kling Team and Dong Chen and Jialu Chen and Xinlong Chen and Yifan Dai and Haotian Dong and Binzong Geng and Yushuo Guan and Minghui Guo and Guanlin He and Guanxiao He and Jingyun Hua and Yanwei Huang and Yiyan Ji and Xueping Jia and Yizhuo Jia and Bozhou Li and Jiaze Li and Min Li and Shihao Li and Shujie Li and Shengyi Liao and Weihong Lin and Shiyuan Liu and Xinyu Liu and Siyang Mao and Xiaoru Qu and Dianmo Sheng and Yang Shi and Jiafu Tang and Longxiang Tang and Yuqi Tang and Chengzhuo Tong and Kuo Wang and Qixun Wang and Rui Wang and Tianduo Wang and Yuran Wang and Zekun Wang and Zihao Wang and Boyang Xia and Yifan Xu and Liu Yang and Zhenlong Yuan and Bohan Zeng and Mujin Zhang and Yuanxing Zhang and Zhicheng Zhang and Zhuoran Zhang and Yuanhong Zheng and Kun Gai and Chengru Song and Pengfei Wan},
year={2026},
eprint={2611.04916},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2611.04916}
}