arXiv:2609.36832v1 Announce Type: new
Abstract: Text-to-video (T2V) diffusion models can generate realistic depictions of actions such as kicking, stabbing, and shooting, raising safety concerns that...
By Ping Liu, Chi Zhang
CleanVideo introduces a selective erasure framework for text-to-video diffusion models, addressing the challenge of removing undesired visual concepts from videos. The method uses a low-dimensional subspace intervention guided by a tri-modal gating mechanism that jointly considers spatiotemporal visual features, timestep signals, and textual semantics to decide where, when, and whether to intervene. Experiments on three video diffusion models demonstrate that CleanVideo effectively erases target concepts while preserving visual fidelity, temporal coherence, and outperforming existing baselines in both frame-level and video-level evaluations, even under concept-recovery attacks.
By Junchi Liao, Hongji Li, Wenrui Zhou, Lijie Hu
arXiv:2609.36598v1 Announce Type: new
Abstract: A video can exhibit convincing motion and photorealism yet fail immediately when visual text collapses. Unlike generic scene content, visual text is un...
By Ziying Zhang, Litao Li, Junchao Liao, Tianyi Zeng, Siyu Zhu, Long Qin, Zhenghao Zhang
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:2609.40356v1 Announce Type: cross
Abstract: Recent video generation is increasingly realistic and controllable, yet video editing remains less developed, particularly for precise local edits th...
By Xinghao Chen, Xiangbo Gao, Jiongze Yu, Yuheng Wu, Zhengzhong Tu
The paper introduces Event-Driven Refresh + Recurrence Memory (EDRRM) to improve Referring Video Object Segmentation (RVOS). EDRRM selectively re-invokes the Sa2VA model at stable change points, using an EMA‑smoothed event score from tracking cues and a recurrence memory that retrieves anchor frames via CLIP similarity. Experiments on Ref‑DAVIS17, MeViS, and ReVOS show that EDRRM maintains or surpasses J&F scores while reducing refresh calls and false‑positive failures, with modest overhead compared to Sa2VA inference.
By Abu Hanif Muhammad Syarubany, Jaehyun Jang, Siwoo Lim, Seungyeon Ryu, Chang D. Yoo