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About this paper
Kling Team, Dong Chen, Jialu Chen, Xinlong Chen, Yifan Dai, Haotian Dong, Binzong Geng, Yushuo Guan and 45 more
The authors present an understanding stack for video generation that links dense captioning, reference-grounded captioning, prompt enhancement, text encoding, reward modeling, and evaluation. Their Kling captioner scores 21.70 on VidCapBench accuracy, while a smaller variant scores 21.84. In one staged prompt-enhancement setup, StoryEval rises from 65.42 to 76.46; DiT-in-the-loop reinforcement learning improves worst-case quality but can reduce best-case output quality.
Summary by alphaXiv.
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