EditStream is a unified DiT‑based framework that supports a wide range of interactive video tasks—Text‑to‑Video, Image‑to‑Video, Video‑to‑Video, Editing Propagation, Reference‑guided Video Editing, and Camera Pose Change—within a single system. It achieves fast, few‑step autoregressive generation by applying a two‑stage distillation process that combines Velocity Moment Matching with autoregressive unrolling, thereby preserving motion quality and temporal stability. The approach aims to make high‑quality diffusion‑based video models practical for real‑time creative workflows.
By Yuqian Zhou, Zhenghong Zhou, Zongze Wu, Cameron Smith, Richard Zhang, Jiebo Luo, Eli Shechtman, Zhe Lin
VIDiff is a unified foundation model that uses diffusion techniques to perform a broad range of video tasks, including both understanding tasks like language‑guided video object segmentation and generative tasks such as video editing and enhancement. Unlike prior methods that focus on short clips and require time‑consuming tuning, VIDiff can edit and translate videos within seconds based on user instructions and employs an iterative auto‑regressive approach to maintain consistency in long‑form videos. The authors demonstrate convincing generative results across diverse input videos and written instructions, supported by qualitative and quantitative evidence.
By Zhen Xing, Shuyuan Tu, Qi Dai, Zihao Zhang, Hui Zhang, Han Hu, Zuxuan Wu, Yu-Gang Jiang
Real-time video editing requires low-latency causal generation with bounded computational resources while preserving source fidelity and long-term temporal consistency. We present JoyAI-Video-Edit, a 16B-parameter autoregressive diffusion framework for real-time, open-ended video editing without access to future frames or a predefined video duration.
BulletTime presents a 4D‑controllable video diffusion framework that separates scene dynamics from camera pose, allowing precise manipulation of both temporal and spatial aspects of generated videos. The model conditions on continuous world‑time sequences and camera trajectories, integrating them via a 4D positional encoding in the attention layer and adaptive normalizations for feature modulation. A specially curated dataset with independently parameterized temporal and camera variations is used for training, and the resulting system demonstrates robust real‑world 4D control while maintaining high generation quality and surpassing prior methods in controllability.
By Yiming Wang, Qihang Zhang, Shengqu Cai, Tong Wu, Jan Ackermann, Zhengfei Kuang, Yang Zheng, Frano Raji\v{c}, Siyu Tang, Gordon Wetzstein
ReaDiT Guidance is a lightweight framework that controls Diffusion Transformer (DiT) models by leveraging internal feature representations from a single DiT block. It steers image and video generation toward spatial targets such as depth, pose, or edge maps, and extends naturally to video generation for camera and motion control. Experiments show competitive or improved results compared to existing feature‑based and adapter‑based methods while using fewer parameters.
By Jay Mahajan, Chang Liu, Rauf Makharov, Viraj Shah, Alexander Schwing, Svetlana Lazebnik
arXiv:2607. 21529v1 Announce Type: cross Abstract: Test-Time Tuning (TTT) on pretrained diffusion models has emerged as a powerful paradigm for video editing.
By Yueyi Liu, Chi Zhang, Sen Cui, Miao Liu
arXiv:2607. 11081v1 Announce Type: cross Abstract: Diffusion Transformers (DiTs) have advanced video generation with high-quality, temporally coherent results.
By Sunyoung Jung, Jiwoo Park, Yoonseok Choi, Kyobin Choo, Ming-Hsuan Yang, Seong Jae Hwang
arXiv:2607. 19895v1 Announce Type: cross Abstract: Text-guided video editing with diffusion models is impractically slow, hindered by costly multi-step sampling and inversion.
By Habin Lim, Gyeong-Moon Park
arXiv:2509.25998v4 Announce Type: replace
Abstract: In light of recent progress in video editing, deep learning models focusing on both spatial and temporal dependencies have emerged as the primary m...
By Abdelilah Aitrouga, Youssef Hmamouche, Amal El Fallah Seghrouchni
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
Autoregressive video diffusion models have emerged as a promising approach for long video generation, achieving strong performance in streaming settings. However, existing methods are restricted to forward temporal generation, whereas practical video creation often requires flexible generation order, e.
Memory-V2V is a memory‑augmented video‑to‑video diffusion framework designed to improve cross‑turn consistency in multi‑turn video editing. It stores previous outputs in an external memory, retrieves relevant edits, and incorporates them via relevance‑aware tokenization and adaptive compression, allowing scalable conditioning without linear computational growth. Experiments on iterative video novel view synthesis and text‑guided long video editing show that Memory‑V2V enhances consistency while preserving visual quality and outperforming strong baselines with modest overhead.
By Dohun Lee, Chun-Hao Paul Huang, Xuelin Chen, Jong Chul Ye, Duygu Ceylan, Hyeonho Jeong