WanPE is a 397‑B parameter prompt‑enhancement model that learns director‑level cinematic planning from 1.05 M real‑world videos. It generates shot‑level cinematic plans through video‑grounded reverse construction and uses Semantic‑Consistency GRPO (SC‑GRPO) to maintain user intent across shots and time. In evaluations, WanPE improves human preference over raw prompts by up to 50.86 points for 30‑second videos and outperforms commercial offerings for shorter durations.
By 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
arXiv:2609.37407v1 Announce Type: new
Abstract: While recent video foundation models excel at generating high-quality short videos, long-form video generation remains a critical challenge, where a ma...
By Xianghan Wei, Xiaoda Yang, Zhi Wang, An Pan, Daoan Zhang, Huayi Zhang, Yan Zhang, Wei Xu, Zishun Liao, Jianwen Lou
SkillPE is a prompt‑engineering framework that evolves reusable cinematic skills from expert‑authored seeds to improve text‑to‑video generation for non‑experts. It encodes shot logic, composition, lighting, sound design, and other filmmaking cues in a fine‑grained format, and uses movie references classified as resonators, dissonants, and divergents to refine skill application and inspire creative alternatives. Experiments on StoryEval and VBench demonstrate up to 1.40‑point gains over the strongest baseline and 0.51 points over seed skills on a 7‑point four‑dimensional evaluation, while remaining competitive on benchmark‑native metrics.
arXiv:2606. 01285v1 Announce Type: cross Abstract: Text-to-video generation has advanced rapidly in visual quality, but remains under-evaluated for factuality and practical usefulness.
By Chenxu Wang, Mingda Chen
arXiv:2608.29621v1 Announce Type: cross
Abstract: Long-horizon story-driven video generation requires a production agent to coordinate narrative decomposition, state tracking, shot design, prompt con...
By Junxiang Liu, Lin Wang, Haiyu Shi, Hongxu Ma, Xiaoyu Yang, Chunjie Chen, Xiaoxiao Xu, Kaiqiao Zhan, Boao Wang, Shuizhou Shi, Tianyun Zhu, Jie Li, Jiangtong Li
arXiv:2610.21758v1 Announce Type: new
Abstract: Recent advances in generative artificial intelligence have accelerated video generation technologies. However, AI-generated videos (AIGVs) remain susce...
By Kehao Zhou, Yixuan Li, Yuxuan Jiang, Fan Zhang