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The recent wave of large-scale text-to-image diffusion models has dramatically increased our text-based image generation abilities. These models can generate realistic images for a staggering variety of prompts and exhibit impressive…

Machine Learning · Computer Science 2023-09-14 Alexander C. Li , Mihir Prabhudesai , Shivam Duggal , Ellis Brown , Deepak Pathak

Match-cuts are powerful cinematic tools that create seamless transitions between scenes, delivering strong visual and metaphorical connections. However, crafting match-cuts is a challenging, resource-intensive process requiring deliberate…

Computer Vision and Pattern Recognition · Computer Science 2024-12-02 Alejandro Pardo , Fabio Pizzati , Tong Zhang , Alexander Pondaven , Philip Torr , Juan Camilo Perez , Bernard Ghanem

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

Zero-shot, training-free, image-based text-to-video generation is an emerging area that aims to generate videos using existing image-based diffusion models. Current methods in this space require specific architectural changes to image…

Computer Vision and Pattern Recognition · Computer Science 2025-04-10 Diljeet Jagpal , Xi Chen , Vinay P. Namboodiri

We present W.A.L.T, a transformer-based approach for photorealistic video generation via diffusion modeling. Our approach has two key design decisions. First, we use a causal encoder to jointly compress images and videos within a unified…

Computer Vision and Pattern Recognition · Computer Science 2023-12-12 Agrim Gupta , Lijun Yu , Kihyuk Sohn , Xiuye Gu , Meera Hahn , Li Fei-Fei , Irfan Essa , Lu Jiang , José Lezama

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

This paper addresses the challenge of text-conditioned streaming motion generation, which requires us to predict the next-step human pose based on variable-length historical motions and incoming texts. Existing methods struggle to achieve…

Computer Vision and Pattern Recognition · Computer Science 2025-08-08 Lixing Xiao , Shunlin Lu , Huaijin Pi , Ke Fan , Liang Pan , Yueer Zhou , Ziyong Feng , Xiaowei Zhou , Sida Peng , Jingbo Wang

Zero-shot customized video generation has gained significant attention due to its substantial application potential. Existing methods rely on additional models to extract and inject reference subject features, assuming that the Video…

Computer Vision and Pattern Recognition · Computer Science 2024-12-31 Tao Wu , Yong Zhang , Xiaodong Cun , Zhongang Qi , Junfu Pu , Huanzhang Dou , Guangcong Zheng , Ying Shan , Xi Li

Controllable generation of 3D human motions becomes an important topic as the world embraces digital transformation. Existing works, though making promising progress with the advent of diffusion models, heavily rely on meticulously captured…

Computer Vision and Pattern Recognition · Computer Science 2024-01-25 Nhat M. Hoang , Kehong Gong , Chuan Guo , Michael Bi Mi

Generative video modeling has emerged as a compelling tool to zero-shot reason about plausible physical interactions for open-world manipulation. Yet, it remains a challenge to translate such human-led motions into the low-level actions…

Robotics · Computer Science 2026-01-01 Karthik Dharmarajan , Wenlong Huang , Jiajun Wu , Li Fei-Fei , Ruohan Zhang

Panoramic video generation has attracted growing attention due to its applications in virtual reality and immersive media. However, existing methods lack explicit motion control and struggle to generate scenes with large and complex…

Computer Vision and Pattern Recognition · Computer Science 2025-12-02 Cheng Zhang , Hanwen Liang , Donny Y. Chen , Qianyi Wu , Konstantinos N. Plataniotis , Camilo Cruz Gambardella , Jianfei Cai

Customized generation using diffusion models has made impressive progress in image generation, but remains unsatisfactory in the challenging video generation task, as it requires the controllability of both subjects and motions. To that…

Computer Vision and Pattern Recognition · Computer Science 2023-12-08 Yujie Wei , Shiwei Zhang , Zhiwu Qing , Hangjie Yuan , Zhiheng Liu , Yu Liu , Yingya Zhang , Jingren Zhou , Hongming Shan

Latent video diffusion models generate videos by progressively transforming Gaussian noise into realistic samples conditioned on text or visual inputs. However, existing conditioning methods often require additional training and…

Computer Vision and Pattern Recognition · Computer Science 2026-05-26 Ofir Abramovich , Nadav Z. Cohen , Adi Rosenthal , Ariel Shamir

Incorporating a temporal dimension into pretrained image diffusion models for video generation is a prevalent approach. However, this method is computationally demanding and necessitates large-scale video datasets. More critically, the…

Computer Vision and Pattern Recognition · Computer Science 2024-08-01 Dengsheng Chen , Jie Hu , Xiaoming Wei , Enhua Wu

Integrating motion into static images not only enhances visual expressiveness but also creates a sense of immersion and temporal depth, establishing it as a longstanding and impactful theme in artistic expression. Fluid elements such as…

Graphics · Computer Science 2025-10-21 Hao Jin , Haoran Xie

Customized video generation aims to generate high-quality videos guided by text prompts and subject's reference images. However, since it is only trained on static images, the fine-tuning process of subject learning disrupts abilities of…

Computer Vision and Pattern Recognition · Computer Science 2024-12-30 Tao Wu , Yong Zhang , Xintao Wang , Xianpan Zhou , Guangcong Zheng , Zhongang Qi , Ying Shan , Xi Li

Recent video-based world models have made pixel-space environments interactive at the camera level: users can navigate viewpoints while the model generates coherent visual continuations. Yet their action spaces remain incomplete: users can…

Computer Vision and Pattern Recognition · Computer Science 2026-05-26 Bohai Gu , Taiyi Wu , Yueyang Yuan , Jian Liu , Xiaocheng Lu , Dazhao Du , Jie Zhang , Jinxiang Lai , Shuai Yang , Xiaotong Zhao , Alan Zhao , Song Guo

Text-to-video diffusion models have enabled high-quality video synthesis, yet often fail to generate temporally coherent and physically plausible motion. A key reason is the models' insufficient understanding of complex motions that natural…

Computer Vision and Pattern Recognition · Computer Science 2025-10-23 Aritra Bhowmik , Denis Korzhenkov , Cees G. M. Snoek , Amirhossein Habibian , Mohsen Ghafoorian

This paper presents a method that allows users to design cinematic video shots in the context of image-to-video generation. Shot design, a critical aspect of filmmaking, involves meticulously planning both camera movements and object…

Computer Vision and Pattern Recognition · Computer Science 2025-02-07 Jinbo Xing , Long Mai , Cusuh Ham , Jiahui Huang , Aniruddha Mahapatra , Chi-Wing Fu , Tien-Tsin Wong , Feng Liu

In this paper, we address the challenge of generating temporally consistent videos with motion guidance. While many existing methods depend on additional control modules or inference-time fine-tuning, recent studies suggest that effective…

Computer Vision and Pattern Recognition · Computer Science 2025-01-14 Xinyu Zhang , Zicheng Duan , Dong Gong , Lingqiao Liu