Paper Detail
Yubo Zhu, Yawen Shao, Ziyun Dai, Zixun Fang, Kai Zhu, Siyang Sun, Haolan Xue, Chuxin Wang, Tingyu Weng, Jingming Luo, Chen Shi, Lianghua Huang, Yufeng Ai, Yuzheng Wang, Wenyuan Zhang, Yu Shang, Yuxiang Bao, Zoubin Bi, Jie Xiao, Jinbo Xing, Jiaxing Zhao, Chongyang Zhong, Hengjian Chen, Chenwei Xie, Akide Liu, Zhehan Kan, Yu Liu, Wei Zhai, Sheng Zhong, Wei Tong
Video generation begins in text space by authoring a cinematic screenplay, then materializes into pixels. As contemporary video generators scale to 30 seconds and faithfully follow complex conditions, the textual prompt largely directs the production, planning how actions, camera trajectories, lighting, and sound unfold across multi-shot sequences. In this paper, we present WanPE, a 397B-parameter prompt enhancement model trained on 1.05M real-world videos to master director-level cinematic planning. WanPE formulates shot-level cinematic plans via video-grounded reverse construction and employs Semantic-Consistency GRPO (SC-GRPO) to faithfully preserve user requirements across shots and over time. To benchmark this capability, we curate WanPEval, a human-annotated testbed covering durations from 5 to 30 seconds across varying intent granularities, supported by approximately 11K blind pairwise assessments. When powering Wan3.0's video generator, WanPE-397B boosts human preference over raw user prompts by 10.66-18.84 points at 5-15 seconds and by a dramatic 50.86 points in the 30-second arena. Ablation studies show that reverse construction demonstrates clear superiority over forward rewriting, while SC-GRPO robustly preserves semantic fidelity across model scales. Ultimately, WanPE leads all evaluated commercial offerings at 5-15 seconds and remains competitive with Seedance 2.5 at 30 seconds.
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@misc{zhu2026wanpe,
title = {WanPE: Towards Cinematic Prompt Enhancement for Modern Text-to-Video Generation},
author = {Yubo Zhu and Yawen Shao and Ziyun Dai and Zixun Fang and Kai Zhu and Siyang Sun and Haolan Xue and Chuxin Wang and Tingyu Weng and Jingming Luo and Chen Shi and Lianghua Huang and Yufeng Ai and Yuzheng Wang and Wenyuan Zhang and Yu Shang and Yuxiang Bao and Zoubin Bi and Jie Xiao and Jinbo Xing and Jiaxing Zhao and Chongyang Zhong and Hengjian Chen and Chenwei Xie and Akide Liu and Zhehan Kan and Yu Liu and Wei Zhai and Sheng Zhong and Wei Tong},
year = {2026},
abstract = {Video generation begins in text space by authoring a cinematic screenplay, then materializes into pixels. As contemporary video generators scale to 30 seconds and faithfully follow complex conditions, the textual prompt largely directs the production, planning how actions, camera trajectories, lighting, and sound unfold across multi-shot sequences. In this paper, we present WanPE, a 397B-parameter prompt enhancement model trained on 1.05M real-world videos to master director-level cinematic plan},
url = {https://huggingface.co/papers/2609.30221},
keywords = {huggingface daily},
eprint = {2609.30221},
archiveprefix = {arXiv},
}
{}