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Related papers: Layered Controllable Video Generation

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Human video generation remains challenging due to the difficulty of jointly modeling human appearance, motion, and camera viewpoint under limited multi-view data. Existing methods often address these factors separately, resulting in limited…

Computer Vision and Pattern Recognition · Computer Science 2026-04-22 Zhengwentai Sun , Keru Zheng , Chenghong Li , Hongjie Liao , Xihe Yang , Heyuan Li , Yihao Zhi , Shuliang Ning , Shuguang Cui , Xiaoguang Han

In this paper, we investigate the generation of new video backgrounds given a human foreground video, a camera pose, and a reference scene image. This task presents three key challenges. First, the generated background should precisely…

Deep learning approaches heavily rely on high-quality human supervision which is nonetheless expensive, time-consuming, and error-prone, especially for image segmentation task. In this paper, we propose a method to automatically synthesize…

Computer Vision and Pattern Recognition · Computer Science 2021-12-06 Yu Yang , Hakan Bilen , Qiran Zou , Wing Yin Cheung , Xiangyang Ji

Advancements in diffusion models have significantly improved video quality, directing attention to fine-grained controllability. However, many existing methods depend on fine-tuning large-scale video models for specific tasks, which becomes…

Computer Vision and Pattern Recognition · Computer Science 2026-03-04 Sangwon Jang , Taekyung Ki , Jaehyeong Jo , Jaehong Yoon , Soo Ye Kim , Zhe Lin , Sung Ju Hwang

Large-scale video generative models can synthesize diverse and realistic visual content for dynamic world creation, but they often lack element-wise controllability, hindering their use in editing scenes and training embodied AI agents. We…

Computer Vision and Pattern Recognition · Computer Science 2025-06-10 Sicheng Mo , Ziyang Leng , Leon Liu , Weizhen Wang , Honglin He , Bolei Zhou

We tackle the long video generation problem, i.e.~generating videos beyond the output length of video generation models. Due to the computation resource constraints, video generation models can only generate video clips that are relatively…

Computer Vision and Pattern Recognition · Computer Science 2023-04-18 Hsin-Ping Huang , Yu-Chuan Su , Ming-Hsuan Yang

While image manipulation achieves tremendous breakthroughs (e.g., generating realistic faces) in recent years, video generation is much less explored and harder to control, which limits its applications in the real world. For instance,…

Computer Vision and Pattern Recognition · Computer Science 2019-08-08 Tsun-Hsuan Wang , Yen-Chi Cheng , Chieh Hubert Lin , Hwann-Tzong Chen , Min Sun

Generating realistic animated videos from static images is an important area of research in computer vision. Methods based on physical simulation and motion prediction have achieved notable advances, but they are often limited to specific…

Computer Vision and Pattern Recognition · Computer Science 2024-05-29 Qiang Wang , Minghua Liu , Junjun Hu , Fan Jiang , Mu Xu

Talking head generation is to generate video based on a given source identity and target motion. However, current methods face several challenges that limit the quality and controllability of the generated videos. First, the generated face…

Computer Vision and Pattern Recognition · Computer Science 2023-11-03 Yue Gao , Yuan Zhou , Jinglu Wang , Xiao Li , Xiang Ming , Yan Lu

We address an essential problem in computer vision, that of unsupervised object segmentation in video, where a main object of interest in a video sequence should be automatically separated from its background. An efficient solution to this…

Computer Vision and Pattern Recognition · Computer Science 2017-04-20 Emanuela Haller , Marius Leordeanu

Creating a vivid video from the event or scenario in our imagination is a truly fascinating experience. Recent advancements in text-to-video synthesis have unveiled the potential to achieve this with prompts only. While text is convenient…

Computer Vision and Pattern Recognition · Computer Science 2023-06-02 Jinbo Xing , Menghan Xia , Yuxin Liu , Yuechen Zhang , Yong Zhang , Yingqing He , Hanyuan Liu , Haoxin Chen , Xiaodong Cun , Xintao Wang , Ying Shan , Tien-Tsin Wong

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

Taking a photo outside, can we predict the immediate future, e.g., how would the cloud move in the sky? We address this problem by presenting a generative adversarial network (GAN) based two-stage approach to generating realistic time-lapse…

Computer Vision and Pattern Recognition · Computer Science 2018-04-02 Wei Xiong , Wenhan Luo , Lin Ma , Wei Liu , Jiebo Luo

Text-to-video generation has advanced rapidly, but existing methods typically output only the final composited video and lack editable layered representations, limiting their use in professional workflows. We propose \textbf{LayerT2V}, a…

Computer Vision and Pattern Recognition · Computer Science 2026-02-27 Guangzhao Li , Kangrui Cen , Baixuan Zhao , Yi Xin , Siqi Luo , Guangtao Zhai , Lei Zhang , Xiaohong Liu

Videos can be created by first outlining a global view of the scene and then adding local details. Inspired by this idea we propose a cascaded model for video generation which follows a coarse to fine approach. First our model generates a…

Computer Vision and Pattern Recognition · Computer Science 2022-06-03 Lluis Castrejon , Nicolas Ballas , Aaron Courville

Controllable video generation remains a significant challenge, despite recent advances in generating high-quality and consistent videos. Most existing methods for controlling video generation treat the video as a whole, neglecting intricate…

Computer Vision and Pattern Recognition · Computer Science 2025-10-07 Yifan Shen , Peiyuan Zhu , Zijian Li , Shaoan Xie , Namrata Deka , Zongfang Liu , Zeyu Tang , Guangyi Chen , Kun Zhang

The generation of sounding videos has seen significant advancements with the advent of diffusion models. However, existing methods often lack the fine-grained control needed to generate viewpoint-specific content from larger, immersive…

Camera control has been actively studied in text or image conditioned video generation tasks. However, altering camera trajectories of a given video remains under-explored, despite its importance in the field of video creation. It is…

Computer Vision and Pattern Recognition · Computer Science 2025-07-10 Jianhong Bai , Menghan Xia , Xiao Fu , Xintao Wang , Lianrui Mu , Jinwen Cao , Zuozhu Liu , Haoji Hu , Xiang Bai , Pengfei Wan , Di Zhang

Layers have become indispensable tools for professional artists, allowing them to build a hierarchical structure that enables independent control over individual visual elements. In this paper, we propose LayeringDiff, a novel pipeline for…

Computer Vision and Pattern Recognition · Computer Science 2025-01-03 Kyoungkook Kang , Gyujin Sim , Geonung Kim , Donguk Kim , Seungho Nam , Sunghyun Cho

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