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We propose FlashWorld, a generative model that produces 3D scenes from a single image or text prompt in seconds, 10~100$\times$ faster than previous works while possessing superior rendering quality. Our approach shifts from the…

Computer Vision and Pattern Recognition · Computer Science 2025-10-16 Xinyang Li , Tengfei Wang , Zixiao Gu , Shengchuan Zhang , Chunchao Guo , Liujuan Cao

This paper proposes the novel task of video generation conditioned on a SINGLE semantic label map, which provides a good balance between flexibility and quality in the generation process. Different from typical end-to-end approaches, which…

Computer Vision and Pattern Recognition · Computer Science 2019-03-12 Junting Pan , Chengyu Wang , Xu Jia , Jing Shao , Lu Sheng , Junjie Yan , Xiaogang Wang

Recent text-to-image models produce high-quality results but still struggle with precise visual control, balancing multimodal inputs, and requiring extensive training for complex multimodal image generation. To address these limitations, we…

Computer Vision and Pattern Recognition · Computer Science 2026-05-29 Haozhe Zhao , Zefan Cai , Shuzheng Si , Liang Chen , Jiuxiang Gu , Wen Xiao , Minjia Zhang , Junjie Hu

Existing keyframe-based motion synthesis mainly focuses on the generation of cyclic actions or short-term motion, such as walking, running, and transitions between close postures. However, these methods will significantly degrade the…

Computer Vision and Pattern Recognition · Computer Science 2022-07-14 Junjun Pan , Siyuan Wang , Junxuan Bai , Ju Dai

The scalability of high-quality online education is hindered by the high costs and slow cycles of manual content creation. Despite advancements in video generation, current approaches often fail to ensure pedagogical structure and precise…

Computers and Society · Computer Science 2026-04-28 Yuheng Wang , Runde Yang , Lin Wu , Jie Zhang , Jingru Fan , Tianle Zhou , Ruoyu Fu , Huatao Li , Ruijie Shi , Siheng Chen , Weinan E , Chen Qian

Generative inbetweening aims to generate intermediate frame sequences by utilizing two key frames as input. Although remarkable progress has been made in video generation models, generative inbetweening still faces challenges in maintaining…

Computer Vision and Pattern Recognition · Computer Science 2024-12-17 Tianyi Zhu , Dongwei Ren , Qilong Wang , Xiaohe Wu , Wangmeng Zuo

Audio-driven talking head generation aims to create vivid and realistic videos from a static portrait and speech. Existing AR-based methods rely on intermediate facial representations, which limit their expressiveness and realism.…

Computer Vision and Pattern Recognition · Computer Science 2026-03-24 Yuzhe Weng , Haotian Wang , Yuanhong Yu , Jun Du , Shan He , Xiaoyan Wu , Haoran Xu

3D Human motion generation is pivotal across film, animation, gaming, and embodied intelligence. Traditional 3D motion synthesis relies on costly motion capture, while recent work shows that 2D videos provide rich, temporally coherent…

Graphics · Computer Science 2026-05-20 Yi-Yang Zhang , Tengjiao Sun , Pengcheng Fang , Deng-Bao Wang , Xiaohao Cai , Min-Ling Zhang , Hansung Kim

Recent multi-modal video generation models have achieved high visual quality, but their prohibitive latency and limited temporal stability hinder real-time deployment. Streaming inference exacerbates these issues, leading to pronounced…

Computer Vision and Pattern Recognition · Computer Science 2026-04-23 Rang Meng , Weipeng Wu , Yuming Li , Chenguang Ma

Multi-modal keyphrase generation aims to produce a set of keyphrases that represent the core points of the input text-image pair. In this regard, dominant methods mainly focus on multi-modal fusion for keyphrase generation. Nevertheless,…

Computer Vision and Pattern Recognition · Computer Science 2023-09-12 Yifan Dong , Suhang Wu , Fandong Meng , Jie Zhou , Xiaoli Wang , Jianxin Lin , Jinsong Su

The synthesis of immersive 3D scenes from text is rapidly maturing, driven by novel video generative models and feed-forward 3D reconstruction, with vast potential in AR/VR and world modeling. While panoramic images have proven effective…

Computer Vision and Pattern Recognition · Computer Science 2026-05-04 Felix Wimbauer , Fabian Manhardt , Michael Oechsle , Nikolai Kalischek , Christian Rupprecht , Daniel Cremers , Federico Tombari

Autoregressive models for video generation typically operate frame-by-frame, extending next-token prediction from language to video's temporal dimension. We question that unlike word as token is universally agreed in language if frame is a…

Computer Vision and Pattern Recognition · Computer Science 2025-09-30 Sucheng Ren , Chen Chen , Zhenbang Wang , Liangchen Song , Xiangxin Zhu , Alan Yuille , Yinfei Yang , Jiasen Lu

With the rise of short video platforms represented by TikTok, the trend of users expressing their creativity through photos and videos has increased dramatically. However, ordinary users lack the professional skills to produce high-quality…

Computer Vision and Pattern Recognition · Computer Science 2024-02-27 Sixiao Zheng , Jingyang Huo , Yu Wang , Yanwei Fu

Creators struggle to edit long-form, narrative-rich videos not because of UI complexity, but due to the cognitive demands of searching, storyboarding, and sequencing hours of footage. Existing transcript- or embedding-based methods fall…

Artificial Intelligence · Computer Science 2025-09-30 Zihan Ding , Xinyi Wang , Junlong Chen , Per Ola Kristensson , Junxiao Shen

Controllable generative models for images and videos have seen significant success, yet 3D scene generation, especially in unbounded scenarios like autonomous driving, remains underdeveloped. Existing methods lack flexible controllability…

Computer Vision and Pattern Recognition · Computer Science 2025-07-28 Ruiyuan Gao , Kai Chen , Zhihao Li , Lanqing Hong , Zhenguo Li , Qiang Xu

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

Generating video stories from text prompts is a complex task. In addition to having high visual quality, videos need to realistically adhere to a sequence of text prompts whilst being consistent throughout the frames. Creating a benchmark…

While text-to-video diffusion models have advanced significantly, creating coherent long-form content remains unreliable due to stochastic sampling artifacts. This necessitates generating multiple candidates, yet verifying them creates a…

Computer Vision and Pattern Recognition · Computer Science 2026-04-09 Daewon Yoon , Hyeongseok Lee , Wonsik Shin , Sangyu Han , Nojun Kwak

Different people speak with diverse personalized speaking styles. Although existing one-shot talking head methods have made significant progress in lip sync, natural facial expressions, and stable head motions, they still cannot generate…

Computer Vision and Pattern Recognition · Computer Science 2023-06-13 Yifeng Ma , Suzhen Wang , Zhipeng Hu , Changjie Fan , Tangjie Lv , Yu Ding , Zhidong Deng , Xin Yu

Text-driven video generation witnesses rapid progress. However, merely using text prompts is not enough to depict the desired subject appearance that accurately aligns with users' intents, especially for customized content creation. In this…

Computer Vision and Pattern Recognition · Computer Science 2023-12-04 Yuming Jiang , Tianxing Wu , Shuai Yang , Chenyang Si , Dahua Lin , Yu Qiao , Chen Change Loy , Ziwei Liu