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:2607. 06481v1 Announce Type: cross Abstract: We present PACR-Video, a parameter-efficient framework for multi-shot long video extrapolation that preserves recurring entities, scene structure, visual style, and causal progression without full generator fine-tuning.
By Anna C\'ordoba, Adam Puente Tercero, Nerea Angulo Hijo, Mar Linares Tercero, Julia Barrientos, Ainhoa Miranda, Jes\'us Olivera
arXiv:2609.36496v1 Announce Type: new
Abstract: Most video editing methods focus on changing the appearance of the source video, while offering limited control over its dynamics. We introduce Reimagi...
By Yu Yuan, Yawen Lu, Guoxian Song, Kevin Duarte, Ratheesh Kalarot, Di Chang, Xijun Wang, Stanley H. Chan
arXiv:2609.37495v1 Announce Type: new
Abstract: Human motion generation plays an important role in applications such as character animation, virtual environments, and embodied interaction. While exis...
By Yun Chen, Munchurl Kim, Jeonghyeok Do
LIFT is a unified image‑to‑video generation framework that adds Layout‑In‑Future control, letting users specify what should appear and where in a future view. It addresses the limitation of existing camera controls and text prompts by using the last‑frame layout as an explicit signal for the desired future scene, especially under large viewpoint changes. To handle sparse layout guidance, LIFT employs on‑policy self‑distillation to transfer knowledge from a dense‑layout teacher to a last‑frame‑layout student, and introduces the LIFT‑Vista dataset with large viewpoint changes and consistent layout annotations. Experiments demonstrate that LIFT improves video quality, future‑layout controllability, and camera controllability compared to other methods.
By Shengxiang Ji, Boyang Wang, Haiyang Xu, Bingnan Li, Yucheng Mao, Zeyuan Chen, Xiaojun Shan, Xiang Zhang, Gang Hua, Jianwen Xie, Zezhou Cheng, Zhuowen Tu
Text-conditioned image-to-video (I2V) generation has advanced rapidly, yet generating videos with multiple subjects remains challenging. A model must simultaneously preserve the appearance of each sub...