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Significant progress has been made in text-to-video generation through the use of powerful generative models and large-scale internet data. However, substantial challenges remain in precisely controlling individual concepts within the…

Computer Vision and Pattern Recognition · Computer Science 2024-09-30 Hanxin Zhu , Tianyu He , Anni Tang , Junliang Guo , Zhibo Chen , Jiang Bian

Camera control is crucial for generating expressive and cinematic videos. Existing methods rely on explicit sequences of camera parameters as control conditions, which can be cumbersome for users to construct, particularly for intricate…

Computer Vision and Pattern Recognition · Computer Science 2025-06-04 Yawen Luo , Jianhong Bai , Xiaoyu Shi , Menghan Xia , Xintao Wang , Pengfei Wan , Di Zhang , Kun Gai , Tianfan Xue

We introduce a novel diffusion-based video generation method, generating a video showing multiple events given multiple individual sentences from the user. Our method does not require a large-scale video dataset since our method uses a…

Computer Vision and Pattern Recognition · Computer Science 2024-07-17 Gyeongrok Oh , Jaehwan Jeong , Sieun Kim , Wonmin Byeon , Jinkyu Kim , Sungwoong Kim , Sangpil Kim

In this paper, we consider a novel and practical case for talking face video generation. Specifically, we focus on the scenarios involving multi-people interactions, where the talking context, such as audience or surroundings, is present.…

Computer Vision and Pattern Recognition · Computer Science 2024-02-29 Meidai Xuanyuan , Yuwang Wang , Honglei Guo , Qionghai Dai

Existing person video generation methods either lack the flexibility in controlling both the appearance and motion, or fail to preserve detailed appearance and temporal consistency. In this paper, we tackle the problem of motion transfer…

Computer Vision and Pattern Recognition · Computer Science 2019-08-13 Kun Cheng , Hao-Zhi Huang , Chun Yuan , Lingyiqing Zhou , Wei Liu

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

Real-world videos consist of sequences of events. Generating such sequences with precise temporal control is infeasible with existing video generators that rely on a single paragraph of text as input. When tasked with generating multiple…

Computer Vision and Pattern Recognition · Computer Science 2025-03-11 Ziyi Wu , Aliaksandr Siarohin , Willi Menapace , Ivan Skorokhodov , Yuwei Fang , Varnith Chordia , Igor Gilitschenski , Sergey Tulyakov

Recent text-to-image generative models can generate high-fidelity images from text prompts. However, these models struggle to consistently generate the same objects in different contexts with the same appearance. Consistent object…

Computer Vision and Pattern Recognition · Computer Science 2023-10-12 Alec Helbling , Evan Montoya , Duen Horng Chau

Current video generation models produce high-quality aesthetic videos but often struggle to learn representations of real-world physics dynamics, resulting in artifacts such as unnatural object collisions, inconsistent gravity, and temporal…

Computer Vision and Pattern Recognition · Computer Science 2026-01-08 Siddarth Nilol Kundur Satish , Devesh Jaiswal , Hongyu Chen , Abhishek Bakshi

Despite recent progress, video diffusion models still struggle to synthesize realistic videos involving highly dynamic motions or requiring fine-grained motion controllability. A central limitation lies in the scarcity of such examples in…

Computer Vision and Pattern Recognition · Computer Science 2026-04-03 Wonjoon Jin , Jiyun Won , Janghyeok Han , Qi Dai , Chong Luo , Seung-Hwan Baek , Sunghyun Cho

Long-horizon action-conditioned video generation aims to synthesize temporally coherent videos that follow complex action instructions over extended horizons, requiring procedural ordering, persistent action execution, and scene consistency…

Computer Vision and Pattern Recognition · Computer Science 2026-05-22 Yu Yang , Yue Liao , Jianbiao Mei , Baisen Wang , Xuemeng Yang , Licheng Wen , Jiangning Zhang , Xiangtai Li , Liang Lv , Hanlin Chen , Botian Shi , Yong Liu , Shuicheng Yan , Gim Hee Lee

Existing text-to-video (T2V) models often struggle with generating videos with sufficiently pronounced or complex actions. A key limitation lies in the text prompt's inability to precisely convey intricate motion details. To address this,…

Computer Vision and Pattern Recognition · Computer Science 2024-11-14 Qiang Zhou , Shaofeng Zhang , Nianzu Yang , Ye Qian , Hao Li

Generating high-quality videos that synthesize desired realistic content is a challenging task due to their intricate high-dimensionality and complexity of videos. Several recent diffusion-based methods have shown comparable performance by…

Computer Vision and Pattern Recognition · Computer Science 2024-04-05 Kihong Kim , Haneol Lee , Jihye Park , Seyeon Kim , Kwanghee Lee , Seungryong Kim , Jaejun Yoo

Although current text-guided music generation technology can cope with simple creative scenarios, achieving fine-grained control over individual text-modality conditions remains challenging as user demands become more intricate.…

Sound · Computer Science 2024-08-12 Jialing Zou , Jiahao Mei , Xudong Nan , Jinghua Li , Daoguo Dong , Liang He

Controllable human video generation aims to produce realistic videos of humans with explicitly guided motions and appearances,serving as a foundation for digital humans, animation, and embodied AI.However, the scarcity of largescale,…

Computer Vision and Pattern Recognition · Computer Science 2026-04-24 Yuanchen Fei , Yude Zou , Zejian Kang , Ming Li , Jiaying Zhou , Xiangru Huang

Recent advances in Video Foundation Models (VFMs) have revolutionized human-centric video synthesis, yet fine-grained and independent editing of subjects and scenes remains a critical challenge. Recent attempts to incorporate richer…

Computer Vision and Pattern Recognition · Computer Science 2026-04-02 Fengyuan Yang , Luying Huang , Jiazhi Guan , Quanwei Yang , Dongwei Pan , Jianglin Fu , Haocheng Feng , Wei He , Kaisiyuan Wang , Hang Zhou , Angela Yao

We present Playable Environments - a new representation for interactive video generation and manipulation in space and time. With a single image at inference time, our novel framework allows the user to move objects in 3D while generating a…

Computer Vision and Pattern Recognition · Computer Science 2022-03-17 Willi Menapace , Stéphane Lathuilière , Aliaksandr Siarohin , Christian Theobalt , Sergey Tulyakov , Vladislav Golyanik , Elisa Ricci

Generative video models are increasingly used in design animation tasks, yet no standardized evaluation framework exists for this domain. Unlike natural video generation, design animation imposes structured constraints: specific components…

Graphics · Computer Science 2026-05-18 Adrienne Deganutti , Dingning Cao , Jaejung Seol , Elad Hirsch , Purvanshi Mehta

Text-guided generative diffusion models unlock powerful image creation and editing tools. While these have been extended to video generation, current approaches that edit the content of existing footage while retaining structure require…

Computer Vision and Pattern Recognition · Computer Science 2023-02-07 Patrick Esser , Johnathan Chiu , Parmida Atighehchian , Jonathan Granskog , Anastasis Germanidis

Spatially consistent long-horizon video generation aims to maintain temporal and spatial consistency along predefined camera trajectories. Existing methods mostly entangle memory modeling with video generation, leading to inconsistent…

Computer Vision and Pattern Recognition · Computer Science 2026-04-22 Yanjun Guo , Zhengqiang Zhang , Pengfei Wang , Xinyue Liang , Zhiyuan Ma , Lei Zhang
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