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We consider the task of Image-to-Video (I2V) generation, which involves transforming static images into realistic video sequences based on a textual description. While recent advancements produce photorealistic outputs, they frequently…

Computer Vision and Pattern Recognition · Computer Science 2025-01-07 Guy Yariv , Yuval Kirstain , Amit Zohar , Shelly Sheynin , Yaniv Taigman , Yossi Adi , Sagie Benaim , Adam Polyak

Recent advances in diffusion models bring new vitality to visual content creation. However, current text-to-video generation models still face significant challenges such as high training costs, substantial data requirements, and…

Computer Vision and Pattern Recognition · Computer Science 2025-03-25 Sicong Feng , Jielong Yang , Li Peng

Advances in video generation have significantly improved the realism and quality of created scenes. This has fueled interest in developing intuitive tools that let users leverage video generation as world simulators. Text-to-video (T2V)…

Computer Vision and Pattern Recognition · Computer Science 2026-04-03 Zuhao Liu , Aleksandar Yanev , Ahmad Mahmood , Ivan Nikolov , Saman Motamed , Wei-Shi Zheng , Xi Wang , Lei Sun , Luc Van Gool , Danda Pani Paudel

Text-conditioned diffusion models have emerged as powerful tools for high-quality video generation. However, enabling Interactive Video Generation (IVG), where users control motion elements such as object trajectory, remains challenging.…

Computer Vision and Pattern Recognition · Computer Science 2025-06-02 Ishaan Rawal , Suryansh Kumar

Recent video generation models have achieved remarkable progress and are now deployed in film, social media production, and advertising. Beyond their creative potential, such models also hold promise as world simulators for robotics and…

Computer Vision and Pattern Recognition · Computer Science 2026-03-24 David Romero , Ariana Bermudez , Viacheslav Iablochnikov , Hao Li , Fabio Pizzati , Ivan Laptev

We present I2V3D, a novel framework for animating static images into dynamic videos with precise 3D control, leveraging the strengths of both 3D geometry guidance and advanced generative models. Our approach combines the precision of a…

Computer Vision and Pattern Recognition · Computer Science 2025-03-14 Zhiyuan Zhang , Dongdong Chen , Jing Liao

We introduce Motion-I2V, a novel framework for consistent and controllable image-to-video generation (I2V). In contrast to previous methods that directly learn the complicated image-to-video mapping, Motion-I2V factorizes I2V into two…

Computer Vision and Pattern Recognition · Computer Science 2024-02-01 Xiaoyu Shi , Zhaoyang Huang , Fu-Yun Wang , Weikang Bian , Dasong Li , Yi Zhang , Manyuan Zhang , Ka Chun Cheung , Simon See , Hongwei Qin , Jifeng Dai , Hongsheng Li

We present visual action prompts, a unified action representation for action-to-video generation of complex high-DoF interactions while maintaining transferable visual dynamics across domains. Action-driven video generation faces a…

Computer Vision and Pattern Recognition · Computer Science 2025-08-19 Yuang Wang , Chao Wen , Haoyu Guo , Sida Peng , Minghan Qin , Hujun Bao , Xiaowei Zhou , Ruizhen Hu

Embodied world models have emerged as a promising paradigm in robotics, most of which leverage large-scale Internet videos or pretrained video generation models to enrich visual and motion priors. However, they still face key challenges: a…

Robotics · Computer Science 2026-02-04 Yixiang Chen , Peiyan Li , Jiabing Yang , Keji He , Xiangnan Wu , Yuan Xu , Kai Wang , Jing Liu , Nianfeng Liu , Yan Huang , Liang Wang

Recent advancements in camera-trajectory-guided image-to-video generation offer higher precision and better support for complex camera control compared to text-based approaches. However, they also introduce significant usability challenges,…

Computer Vision and Pattern Recognition · Computer Science 2025-07-15 Teng Li , Guangcong Zheng , Rui Jiang , Shuigen Zhan , Tao Wu , Yehao Lu , Yining Lin , Chuanyun Deng , Yepan Xiong , Min Chen , Lin Cheng , Xi Li

Generating realistic 3D human-human interactions from textual descriptions remains a challenging task. Existing approaches, typically based on diffusion models, often produce results lacking realism and fidelity. In this work, we introduce…

Computer Vision and Pattern Recognition · Computer Science 2025-03-04 Muhammad Gohar Javed , Chuan Guo , Li Cheng , Xingyu Li

We introduce $\textit{InteractiveVideo}$, a user-centric framework for video generation. Different from traditional generative approaches that operate based on user-provided images or text, our framework is designed for dynamic interaction,…

Computer Vision and Pattern Recognition · Computer Science 2024-02-06 Yiyuan Zhang , Yuhao Kang , Zhixin Zhang , Xiaohan Ding , Sanyuan Zhao , Xiangyu Yue

Synthesizing realistic human-object interactions (HOI) in video is challenging due to the complex, instance-specific interaction dynamics of both humans and objects. Incorporating controllability in video generation further adds to the…

Computer Vision and Pattern Recognition · Computer Science 2026-04-09 Wanyue Zhang , Lin Geng Foo , Thabo Beeler , Rishabh Dabral , Christian Theobalt

We tackle the challenges of synthesizing versatile, physically simulated human motions for full-body object manipulation. Unlike prior methods that are focused on detailed motion tracking, trajectory following, or teleoperation, our…

Robotics · Computer Science 2025-12-12 Chen Tessler , Yifeng Jiang , Erwin Coumans , Zhengyi Luo , Gal Chechik , Xue Bin Peng

The ability to predict future visual observations conditioned on past observations and motor commands can enable embodied agents to plan solutions to a variety of tasks in complex environments. This work shows that we can create good video…

Computer Vision and Pattern Recognition · Computer Science 2022-08-09 Agrim Gupta , Stephen Tian , Yunzhi Zhang , Jiajun Wu , Roberto Martín-Martín , Li Fei-Fei

Image-to-video (I2V) generation seeks to produce realistic motion sequences from a single reference image. Although recent methods exhibit strong temporal consistency, they often struggle when dealing with complex, non-repetitive human…

Computer Vision and Pattern Recognition · Computer Science 2025-07-01 Ashkan Taghipour , Morteza Ghahremani , Mohammed Bennamoun , Farid Boussaid , Aref Miri Rekavandi , Zinuo Li , Qiuhong Ke , Hamid Laga

Generating controllable videos conforming to user intentions is an appealing yet challenging topic in computer vision. To enable maneuverable control in line with user intentions, a novel video generation task, named Text-Image-to-Video…

Computer Vision and Pattern Recognition · Computer Science 2022-04-01 Yaosi Hu , Chong Luo , Zhenzhong Chen

Recent advances in video diffusion models shows promise for generating robotic decision-making data, with trajectory conditions further enabling fine-grained control. However, existing methods primarily focus on individual object motion and…

Computer Vision and Pattern Recognition · Computer Science 2026-01-28 Xiao Fu , Xintao Wang , Xian Liu , Jianhong Bai , Runsen Xu , Pengfei Wan , Di Zhang , Dahua Lin

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 long, high-quality videos remains a challenge due to the complex interplay of spatial and temporal dynamics and hardware limitations. In this work, we introduce MaskFlow, a unified video generation framework that combines…

Computer Vision and Pattern Recognition · Computer Science 2025-03-13 Michael Fuest , Vincent Tao Hu , Björn Ommer
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