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This technical report presents a cost-efficient strategy for training a video generation foundation model. We present a mid-sized research model with approximately 7 billion parameters (7B) called Seaweed-7B trained from scratch using…

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

In recent years, video generation has seen significant advancements. However, challenges still persist in generating complex motions and interactions. To address these challenges, we introduce ReVision, a plug-and-play framework that…

Computer Vision and Pattern Recognition · Computer Science 2026-01-12 Qihao Liu , Ju He , Qihang Yu , Liang-Chieh Chen , Alan Yuille

Generating flexible-view 3D scenes, including 360{\deg} rotation and zooming, from single images is challenging due to a lack of 3D data. To this end, we introduce FlexWorld, a novel framework consisting of two key components: (1) a strong…

Computer Vision and Pattern Recognition · Computer Science 2025-03-20 Luxi Chen , Zihan Zhou , Min Zhao , Yikai Wang , Ge Zhang , Wenhao Huang , Hao Sun , Ji-Rong Wen , Chongxuan Li

Video foundation models aim to integrate video understanding, generation, editing, and instruction following within a single framework, making them a central direction for next-generation multimodal systems. However, existing evaluation…

Computer Vision and Pattern Recognition · Computer Science 2026-03-09 Jianhui Wei , Xiaotian Zhang , Yichen Li , Yuan Wang , Yan Zhang , Ziyi Chen , Zhihang Tang , Wei Xu , Zuozhu Liu

The recent wave of AI-generated content (AIGC) has witnessed substantial success in computer vision, with the diffusion model playing a crucial role in this achievement. Due to their impressive generative capabilities, diffusion models are…

Computer Vision and Pattern Recognition · Computer Science 2024-09-17 Zhen Xing , Qijun Feng , Haoran Chen , Qi Dai , Han Hu , Hang Xu , Zuxuan Wu , Yu-Gang Jiang

We present Yan, a foundational framework for interactive video generation, covering the entire pipeline from simulation and generation to editing. Specifically, Yan comprises three core modules. AAA-level Simulation: We design a…

Computer Vision and Pattern Recognition · Computer Science 2025-08-15 Deheng Ye , Fangyun Zhou , Jiacheng Lv , Jianqi Ma , Jun Zhang , Junyan Lv , Junyou Li , Minwen Deng , Mingyu Yang , Qiang Fu , Wei Yang , Wenkai Lv , Yangbin Yu , Yewen Wang , Yonghang Guan , Zhihao Hu , Zhongbin Fang , Zhongqian Sun

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

Video generation has seen remarkable progress thanks to advancements in generative deep learning. However, generating long sequences remains a significant challenge. Generated videos should not only display coherent and continuous movement…

Computer Vision and Pattern Recognition · Computer Science 2026-01-01 Jingbo Yang , Adrian G. Bors

Vision and language are the two foundational senses for humans, and they build up our cognitive ability and intelligence. While significant breakthroughs have been made in AI language ability, artificial visual intelligence, especially the…

Computer Vision and Pattern Recognition · Computer Science 2024-12-31 Zangwei Zheng , Xiangyu Peng , Tianji Yang , Chenhui Shen , Shenggui Li , Hongxin Liu , Yukun Zhou , Tianyi Li , Yang You

We present Omni-Video 2, a scalable and computationally efficient model that connects pretrained multimodal large-language models (MLLMs) with video diffusion models for unified video generation and editing. Our key idea is to exploit the…

Computer Vision and Pattern Recognition · Computer Science 2026-03-16 Hao Yang , Zhiyu Tan , Jia Gong , Luozheng Qin , Hesen Chen , Xiaomeng Yang , Yuqing Sun , Yuetan Lin , Mengping Yang , Hao Li

Generating temporally coherent high fidelity video is an important milestone in generative modeling research. We make progress towards this milestone by proposing a diffusion model for video generation that shows very promising initial…

Computer Vision and Pattern Recognition · Computer Science 2022-06-24 Jonathan Ho , Tim Salimans , Alexey Gritsenko , William Chan , Mohammad Norouzi , David J. Fleet

We present Qwen-Image-2.0, an omni-capable image generation foundation model that unifies high-fidelity generation and precise image editing within a single framework. Despite recent progress, existing models still struggle with ultra-long…

Recent advancements in generative models have provided promising solutions for synthesizing realistic driving videos, which are crucial for training autonomous driving perception models. However, existing approaches often struggle with…

Computer Vision and Pattern Recognition · Computer Science 2024-09-13 Wei Wu , Xi Guo , Weixuan Tang , Tingxuan Huang , Chiyu Wang , Dongyue Chen , Chenjing Ding

Human-centric generative models are becoming increasingly popular, giving rise to various innovative tools and applications, such as talking face videos conditioned on text or audio prompts. The core of these capabilities lies in powerful…

Computer Vision and Pattern Recognition · Computer Science 2025-07-15 Donglin Di , He Feng , Wenzhang Sun , Yongjia Ma , Hao Li , Wei Chen , Lei Fan , Tonghua Su , Xun Yang

High-resolution video generation has emerged as a crucial task in computer vision, with wide-ranging applications in entertainment, simulation, and data augmentation. However, generating temporally coherent and visually realistic videos…

Image and Video Processing · Electrical Eng. & Systems 2025-07-08 Abhinav Sagar

Text-to-video generative models convert textual prompts into dynamic visual content, offering wide-ranging applications in film production, gaming, and education. However, their real-world performance often falls short of user expectations.…

Computer Vision and Pattern Recognition · Computer Science 2025-05-14 Wenhao Wang , Yi Yang

Recently, video generation techniques have advanced rapidly. Given the popularity of video content on social media platforms, these models intensify concerns about the spread of fake information. Therefore, there is a growing demand for…

Computer Vision and Pattern Recognition · Computer Science 2024-08-23 Haoxing Chen , Yan Hong , Zizheng Huang , Zhuoer Xu , Zhangxuan Gu , Yaohui Li , Jun Lan , Huijia Zhu , Jianfu Zhang , Weiqiang Wang , Huaxiong Li

General-purpose robots require decision-making models that generalize across diverse tasks and environments. Recent works build robot foundation models by extending multimodal large language models (MLLMs) with action outputs, creating…

Video generation has achieved rapid progress benefiting from high-quality renderings provided by powerful image generators. We regard the video synthesis task as generating a sequence of images sharing the same contents but varying in…

Computer Vision and Pattern Recognition · Computer Science 2023-03-07 Jingyuan Zhu , Huimin Ma , Jiansheng Chen , Jian Yuan