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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

Storyboard synthesis plays a crucial role in visual storytelling, aiming to generate coherent shot sequences that visually narrate cinematic events with consistent characters, scenes, and transitions. However, existing approaches are mostly…

Computer Vision and Pattern Recognition · Computer Science 2026-04-21 Junjia Huang , Binbin Yang , Pengxiang Yan , Jiyang Liu , Bin Xia , Zhao Wang , Yitong Wang , Liang Lin , Guanbin Li

Video generation models have advanced significantly, yet they still struggle to synthesize complex human movements due to the high degrees of freedom in human articulation. This limitation stems from the intrinsic constraints of pixel-only…

Computer Vision and Pattern Recognition · Computer Science 2025-12-23 Yuxiao Yang , Hualian Sheng , Sijia Cai , Jing Lin , Jiahao Wang , Bing Deng , Junzhe Lu , Haoqian Wang , Jieping Ye

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

Video-based world models hold significant potential for generating high-quality embodied manipulation data. However, current video generation methods struggle to achieve stable long-horizon generation: classical diffusion-based approaches…

Computer Vision and Pattern Recognition · Computer Science 2025-09-29 Yu Shang , Lei Jin , Yiding Ma , Xin Zhang , Chen Gao , Wei Wu , Yong Li

Generating long-form audio-visual stories from a short user prompt remains challenging due to an intent-execution gap, where high-level narrative intent must be preserved across coherent, shot-level multimodal generation over long horizons.…

Computer Vision and Pattern Recognition · Computer Science 2026-02-04 Wenzhang Sun , Zhenyu Wang , Zhangchi Hu , Chunfeng Wang , Hao Li , Wei Chen

Video diffusion models substantially boost the productivity of artistic workflows with high-quality portrait video generative capacity. However, prevailing pipelines are primarily constrained to single-shot creation, while real-world…

Computer Vision and Pattern Recognition · Computer Science 2025-06-23 Jiahao Wang , Hualian Sheng , Sijia Cai , Weizhan Zhang , Caixia Yan , Yachuang Feng , Bing Deng , Jieping Ye

By generating plausible and smooth transitions between two image frames, video inbetweening is an essential tool for video editing and long video synthesis. Traditional works lack the capability to generate complex large motions. While…

Computer Vision and Pattern Recognition · Computer Science 2025-01-09 Maham Tanveer , Yang Zhou , Simon Niklaus , Ali Mahdavi Amiri , Hao Zhang , Krishna Kumar Singh , Nanxuan Zhao

Recent advancements in video diffusion models have shown exceptional abilities in simulating real-world dynamics and maintaining 3D consistency. This progress inspires us to investigate the potential of these models to ensure dynamic…

Computer Vision and Pattern Recognition · Computer Science 2024-12-11 Jianhong Bai , Menghan Xia , Xintao Wang , Ziyang Yuan , Xiao Fu , Zuozhu Liu , Haoji Hu , Pengfei Wan , Di Zhang

State-of-the-art text-to-video models excel at generating isolated clips but fall short of creating the coherent, multi-shot narratives, which are the essence of storytelling. We bridge this "narrative gap" with HoloCine, a model that…

Computer Vision and Pattern Recognition · Computer Science 2025-10-24 Yihao Meng , Hao Ouyang , Yue Yu , Qiuyu Wang , Wen Wang , Ka Leong Cheng , Hanlin Wang , Yixuan Li , Cheng Chen , Yanhong Zeng , Yujun Shen , Huamin Qu

We present TempoMaster, a novel framework that formulates long video generation as next-frame-rate prediction. Specifically, we first generate a low-frame-rate clip that serves as a coarse blueprint of the entire video sequence, and then…

Computer Vision and Pattern Recognition · Computer Science 2025-12-03 Yukuo Ma , Cong Liu , Junke Wang , Junqi Liu , Haibin Huang , Zuxuan Wu , Chi Zhang , Xuelong Li

Training a generative model on a single human skeletal motion sequence without being bound to a specific kinematic tree has drawn significant attention from the animation community. Unlike text-to-motion generation, single-shot models allow…

Graphics · Computer Science 2025-08-27 Eleni Tselepi , Spyridon Thermos , Gerasimos Potamianos

State-of-the-art video generation models produce remarkable photorealism, but they lack the precise control required to align generated content with specific scene requirements. Furthermore, without an underlying explicit geometry, these…

Computer Vision and Pattern Recognition · Computer Science 2026-03-25 Dana Cohen-Bar , Ido Sobol , Raphael Bensadoun , Shelly Sheynin , Oran Gafni , Or Patashnik , Daniel Cohen-Or , Amit Zohar

Video storytelling is engaging multimedia content that utilizes video and its accompanying narration to attract the audience, where a key challenge is creating narrations for recorded visual scenes. Previous studies on dense video…

Multimedia · Computer Science 2024-12-31 Dingyi Yang , Chunru Zhan , Ziheng Wang , Biao Wang , Tiezheng Ge , Bo Zheng , Qin Jin

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

Autoregressive diffusion enables real-time frame streaming, yet existing sliding-window caches discard past context, causing fidelity degradation, identity drift, and motion stagnation over long horizons. Current approaches preserve a fixed…

Computer Vision and Pattern Recognition · Computer Science 2026-03-16 Youngrae Kim , Qixin Hu , C. -C. Jay Kuo , Peter A. Beerel

Maintaining consistent characters, props, and environments across multiple shots is a central challenge in narrative video generation. Existing models can produce high-quality short clips but often fail to preserve entity identity and…

Computer Vision and Pattern Recognition · Computer Science 2026-01-08 Jinsong Zhou , Yihua Du , Xinli Xu , Luozhou Wang , Zijie Zhuang , Yehang Zhang , Shuaibo Li , Xiaojun Hu , Bolan Su , Ying-cong Chen

Audio-driven human animation methods, such as talking head and talking body generation, have made remarkable progress in generating synchronized facial movements and appealing visual quality videos. However, existing methods primarily focus…

Computer Vision and Pattern Recognition · Computer Science 2025-05-29 Zhe Kong , Feng Gao , Yong Zhang , Zhuoliang Kang , Xiaoming Wei , Xunliang Cai , Guanying Chen , Wenhan Luo

This paper aims to manipulate multi-entity 3D motions in video generation. Previous methods on controllable video generation primarily leverage 2D control signals to manipulate object motions and have achieved remarkable synthesis results.…

Computer Vision and Pattern Recognition · Computer Science 2025-07-08 Xiao Fu , Xian Liu , Xintao Wang , Sida Peng , Menghan Xia , Xiaoyu Shi , Ziyang Yuan , Pengfei Wan , Di Zhang , Dahua Lin

Talking head generation is to generate video based on a given source identity and target motion. However, current methods face several challenges that limit the quality and controllability of the generated videos. First, the generated face…

Computer Vision and Pattern Recognition · Computer Science 2023-11-03 Yue Gao , Yuan Zhou , Jinglu Wang , Xiao Li , Xiang Ming , Yan Lu