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The goal of conditional image-to-video (cI2V) generation is to create a believable new video by beginning with the condition, i.e., one image and text.The previous cI2V generation methods conventionally perform in RGB pixel space, with…

Computer Vision and Pattern Recognition · Computer Science 2023-12-15 Cuifeng Shen , Yulu Gan , Chen Chen , Xiongwei Zhu , Lele Cheng , Tingting Gao , Jinzhi Wang

Video-based world models have recently garnered increasing attention for their ability to synthesize diverse and dynamic visual environments. In this paper, we focus on shared world modeling, where a model generates multiple videos from a…

Computer Vision and Pattern Recognition · Computer Science 2025-12-03 Fan Wu , Jiacheng Wei , Ruibo Li , Yi Xu , Junyou Li , Deheng Ye , Guosheng Lin

The "one-shot" technique represents a distinct and sophisticated aesthetic in filmmaking. However, its practical realization is often hindered by prohibitive costs and complex real-world constraints. Although emerging video generation…

Computer Vision and Pattern Recognition · Computer Science 2025-12-29 Jiawei Liu , Junqiao Li , Jiangfan Deng , Gen Li , Siyu Zhou , Zetao Fang , Shanshan Lao , Zengde Deng , Jianing Zhu , Tingting Ma , Jiayi Li , Yunqiu Wang , Qian He , Xinglong Wu

We present WonderWorld, a novel framework for interactive 3D scene generation that enables users to interactively specify scene contents and layout and see the created scenes in low latency. The major challenge lies in achieving fast…

Computer Vision and Pattern Recognition · Computer Science 2025-03-26 Hong-Xing Yu , Haoyi Duan , Charles Herrmann , William T. Freeman , Jiajun Wu

Recent video generators achieve striking photorealism, yet remain fundamentally inconsistent in 3D. We present WorldReel, a 4D video generator that is natively spatio-temporally consistent. WorldReel jointly produces RGB frames together…

Computer Vision and Pattern Recognition · Computer Science 2025-12-09 Shaoheng Fang , Hanwen Jiang , Yunpeng Bai , Niloy J. Mitra , Qixing Huang

Video Generation is a relatively new and yet popular subject in machine learning due to its vast variety of potential applications and its numerous challenges. Current methods in Video Generation provide the user with little or no control…

Computer Vision and Pattern Recognition · Computer Science 2021-11-22 Bahman Rouhani , Mohammad Rahmati

Video virtual try-on aims to generate realistic sequences that maintain garment identity and adapt to a person's pose and body shape in source videos. Traditional image-based methods, relying on warping and blending, struggle with complex…

Computer Vision and Pattern Recognition · Computer Science 2024-07-16 Zijian He , Peixin Chen , Guangrun Wang , Guanbin Li , Philip H. S. Torr , Liang Lin

Physical principles are fundamental to realistic visual simulation, but remain a significant oversight in transformer-based video generation. This gap highlights a critical limitation in rendering rigid body motion, a core tenet of…

Computer Vision and Pattern Recognition · Computer Science 2026-01-19 Qiyuan Zhang , Biao Gong , Shuai Tan , Zheng Zhang , Yujun Shen , Xing Zhu , Yuyuan Li , Kelu Yao , Chunhua Shen , Changqing Zou

Despite remarkable achievements in video synthesis, achieving granular control over complex dynamics, such as nuanced movement among multiple interacting objects, still presents a significant hurdle for dynamic world modeling, compounded by…

Computer Vision and Pattern Recognition · Computer Science 2024-03-21 Pengxiang Li , Kai Chen , Zhili Liu , Ruiyuan Gao , Lanqing Hong , Guo Zhou , Hua Yao , Dit-Yan Yeung , Huchuan Lu , Xu Jia

Generating realistic and controllable weather effects in videos is valuable for many applications. Physics-based weather simulation requires precise reconstructions that are hard to scale to in-the-wild videos, while current video editing…

Graphics · Computer Science 2025-07-22 Chih-Hao Lin , Zian Wang , Ruofan Liang , Yuxuan Zhang , Sanja Fidler , Shenlong Wang , Zan Gojcic

Generating videos with realistic and physically plausible motion is one of the main recent challenges in computer vision. While diffusion models are achieving compelling results in image generation, video diffusion models are limited by…

Machine Learning · Computer Science 2024-10-28 Luca Savant Aira , Antonio Montanaro , Emanuele Aiello , Diego Valsesia , Enrico Magli

Modern video diffusion models excel at appearance synthesis but still struggle with physical consistency: objects drift, collisions lack realistic rebound, and material responses seldom match their underlying properties. We present PhyCo, a…

Computer Vision and Pattern Recognition · Computer Science 2026-05-01 Sriram Narayanan , Ziyu Jiang , Srinivasa Narasimhan , Manmohan Chandraker

Camera-controlled video generation has achieved remarkable progress in recent years. However, existing video-to-video re-rendering methods primarily rely on Supervised Fine-Tuning using synthetic datasets. At present, there is an extreme…

Computer Vision and Pattern Recognition · Computer Science 2026-05-25 Zizun Li , Haoyu Guo , Runzhe Teng , Chunhua Shen , Tong He

The video generation field has witnessed rapid improvements with the introduction of recent diffusion models. While these models have successfully enhanced appearance quality, they still face challenges in generating coherent and natural…

Computer Vision and Pattern Recognition · Computer Science 2025-04-21 Yaosi Hu , Zhenzhong Chen , Chong Luo

We introduce VividDream, a method for generating explorable 4D scenes with ambient dynamics from a single input image or text prompt. VividDream first expands an input image into a static 3D point cloud through iterative inpainting and…

Computer Vision and Pattern Recognition · Computer Science 2024-05-31 Yao-Chih Lee , Yi-Ting Chen , Andrew Wang , Ting-Hsuan Liao , Brandon Y. Feng , Jia-Bin Huang

Recent advancements in diffusion models have set new benchmarks in image and video generation, enabling realistic visual synthesis across single- and multi-frame contexts. However, these models still struggle with efficiently and explicitly…

Computer Vision and Pattern Recognition · Computer Science 2024-12-03 Qihang Zhang , Shuangfei Zhai , Miguel Angel Bautista , Kevin Miao , Alexander Toshev , Joshua Susskind , Jiatao Gu

Video generative models pre-trained on large-scale internet datasets have achieved remarkable success, excelling at producing realistic synthetic videos. However, they often generate clips based on static prompts (e.g., text or images),…

Computer Vision and Pattern Recognition · Computer Science 2025-02-13 Haoran He , Yang Zhang , Liang Lin , Zhongwen Xu , Ling Pan

Recent generative video world models aim to simulate visual environment evolution, allowing an observer to interactively explore the scene via camera control. However, they implicitly assume that the world only evolves within the observer's…

Computer Vision and Pattern Recognition · Computer Science 2026-03-25 Zicheng Duan , Jiatong Xia , Zeyu Zhang , Wenbo Zhang , Gengze Zhou , Chenhui Gou , Yefei He , Feng Chen , Xinyu Zhang , Lingqiao Liu

One-shot 3D talking portrait generation aims to reconstruct a 3D avatar from an unseen image, and then animate it with a reference video or audio to generate a talking portrait video. The existing methods fail to simultaneously achieve the…

Computer Vision and Pattern Recognition · Computer Science 2024-03-26 Zhenhui Ye , Tianyun Zhong , Yi Ren , Jiaqi Yang , Weichuang Li , Jiawei Huang , Ziyue Jiang , Jinzheng He , Rongjie Huang , Jinglin Liu , Chen Zhang , Xiang Yin , Zejun Ma , Zhou Zhao

We explore a novel video creation experience, namely Video Creation by Demonstration. Given a demonstration video and a context image from a different scene, we generate a physically plausible video that continues naturally from the context…

Computer Vision and Pattern Recognition · Computer Science 2024-12-13 Yihong Sun , Hao Zhou , Liangzhe Yuan , Jennifer J. Sun , Yandong Li , Xuhui Jia , Hartwig Adam , Bharath Hariharan , Long Zhao , Ting Liu
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