English
Related papers

Related papers: MotionStream: Real-Time Video Generation with Inte…

200 papers

Generative models are reshaping the live-streaming industry by redefining how content is created, styled, and delivered. Previous image-based streaming diffusion models have powered efficient and creative live streaming products but have…

Computer Vision and Pattern Recognition · Computer Science 2026-02-24 Tianrui Feng , Zhi Li , Shuo Yang , Haocheng Xi , Muyang Li , Xiuyu Li , Lvmin Zhang , Keting Yang , Kelly Peng , Song Han , Maneesh Agrawala , Kurt Keutzer , Akio Kodaira , Chenfeng Xu

Long video generation remains a challenging and compelling topic in computer vision. Diffusion based models, among the various approaches to video generation, have achieved state of the art quality with their iterative denoising procedures.…

Computer Vision and Pattern Recognition · Computer Science 2025-03-14 Siyang Zhang , Harry Yang , Ser-Nam Lim

Immersive video offers a 6-Dof-free viewing experience, potentially playing a key role in future video technology. Recently, 4D Gaussian Splatting has gained attention as an effective approach for immersive video due to its high rendering…

Computer Vision and Pattern Recognition · Computer Science 2025-05-13 Hao Li , Sicheng Li , Xiang Gao , Abudouaihati Batuer , Lu Yu , Yiyi Liao

This paper presents WorldPlay, a streaming video diffusion model that enables real-time, interactive world modeling with long-term geometric consistency, resolving the trade-off between speed and memory that limits current methods.…

Computer Vision and Pattern Recognition · Computer Science 2025-12-17 Wenqiang Sun , Haiyu Zhang , Haoyuan Wang , Junta Wu , Zehan Wang , Zhenwei Wang , Yunhong Wang , Jun Zhang , Tengfei Wang , Chunchao Guo

With the advance of diffusion models, today's video generation has achieved impressive quality. But generating temporal consistent long videos is still challenging. A majority of video diffusion models (VDMs) generate long videos in an…

Computer Vision and Pattern Recognition · Computer Science 2024-06-18 Kaifeng Gao , Jiaxin Shi , Hanwang Zhang , Chunping Wang , Jun Xiao

Diffusion-based video generation has achieved significant progress, yet generating multiple actions that occur sequentially remains a formidable task. Directly generating a video with sequential actions can be extremely challenging due to…

Computer Vision and Pattern Recognition · Computer Science 2024-05-29 Bowen Zhang , Xiaofei Xie , Haotian Lu , Na Ma , Tianlin Li , Qing Guo

We introduce a novel diffusion-based video generation method, generating a video showing multiple events given multiple individual sentences from the user. Our method does not require a large-scale video dataset since our method uses a…

Computer Vision and Pattern Recognition · Computer Science 2024-07-17 Gyeongrok Oh , Jaehwan Jeong , Sieun Kim , Wonmin Byeon , Jinkyu Kim , Sungwoong Kim , Sangpil Kim

Research on diffusion model-based video generation has advanced rapidly. However, limitations in object fidelity and generation length hinder its practical applications. Additionally, specific domains like animated wallpapers require…

Computer Vision and Pattern Recognition · Computer Science 2024-04-17 Fanyi Wang , Peng Liu , Haotian Hu , Dan Meng , Jingwen Su , Jinjin Xu , Yanhao Zhang , Xiaoming Ren , Zhiwang Zhang

While image manipulation achieves tremendous breakthroughs (e.g., generating realistic faces) in recent years, video generation is much less explored and harder to control, which limits its applications in the real world. For instance,…

Computer Vision and Pattern Recognition · Computer Science 2019-08-08 Tsun-Hsuan Wang , Yen-Chi Cheng , Chieh Hubert Lin , Hwann-Tzong Chen , Min Sun

Recent advances in diffusion models have improved controllable streetscape generation and supported downstream perception and planning tasks. However, challenges remain in accurately modeling driving scenes and generating long videos. To…

Computer Vision and Pattern Recognition · Computer Science 2025-05-30 Jianbiao Mei , Tao Hu , Xuemeng Yang , Licheng Wen , Yu Yang , Tiantian Wei , Yukai Ma , Min Dou , Botian Shi , Yong Liu

Existing person video generation methods either lack the flexibility in controlling both the appearance and motion, or fail to preserve detailed appearance and temporal consistency. In this paper, we tackle the problem of motion transfer…

Computer Vision and Pattern Recognition · Computer Science 2019-08-13 Kun Cheng , Hao-Zhi Huang , Chun Yuan , Lingyiqing Zhou , Wei Liu

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

Recently, diffusion models like StableDiffusion have achieved impressive image generation results. However, the generation process of such diffusion models is uncontrollable, which makes it hard to generate videos with continuous and…

Computer Vision and Pattern Recognition · Computer Science 2023-08-04 Zhihao Hu , Dong Xu

Traditional 3D content creation tools empower users to bring their imagination to life by giving them direct control over a scene's geometry, appearance, motion, and camera path. Creating computer-generated videos, however, is a tedious…

Computer Vision and Pattern Recognition · Computer Science 2023-12-05 Shengqu Cai , Duygu Ceylan , Matheus Gadelha , Chun-Hao Paul Huang , Tuanfeng Yang Wang , Gordon Wetzstein

The core challenge for streaming video generation is maintaining the content consistency in long context, which poses high requirement for the memory design. Most existing solutions maintain the memory by compressing historical frames with…

Computer Vision and Pattern Recognition · Computer Science 2025-12-17 Sihui Ji , Xi Chen , Shuai Yang , Xin Tao , Pengfei Wan , Hengshuang Zhao

Current frontier video diffusion models have demonstrated remarkable results at generating high-quality videos. However, they can only generate short video clips, normally around 10 seconds or 240 frames, due to computation limitations…

Computer Vision and Pattern Recognition · Computer Science 2025-05-20 Desai Xie , Zhan Xu , Yicong Hong , Hao Tan , Difan Liu , Feng Liu , Arie Kaufman , Yang Zhou

Modeling scenes using video generation models has garnered growing research interest in recent years. However, most existing approaches rely on perspective video models that synthesize only limited observations of a scene, leading to issues…

Computer Vision and Pattern Recognition · Computer Science 2026-04-01 Yuheng Liu , Xin Lin , Xinke Li , Baihan Yang , Chen Wang , Kalyan Sunkavalli , Yannick Hold-Geoffroy , Hao Tan , Kai Zhang , Xiaohui Xie , Zifan Shi , Yiwei Hu

Existing DiT-based audio-driven avatar generation methods have achieved considerable progress, yet their broader application is constrained by limitations such as high computational overhead and the inability to synthesize long-duration…

Computer Vision and Pattern Recognition · Computer Science 2026-04-01 Chaochao Li , Ruikui Wang , Liangbo Zhou , Jinheng Feng , Huaishao Luo , Huan Zhang , Youzheng Wu , Xiaodong He

Text-to-video diffusion models enable the generation of high-quality videos that follow text instructions, making it easy to create diverse and individual content. However, existing approaches mostly focus on high-quality short video…

Computer Vision and Pattern Recognition · Computer Science 2025-04-17 Roberto Henschel , Levon Khachatryan , Hayk Poghosyan , Daniil Hayrapetyan , Vahram Tadevosyan , Zhangyang Wang , Shant Navasardyan , Humphrey Shi

Vision-language models (VLMs) could power real-time assistants and autonomous agents, but they face a critical challenge: understanding near-infinite video streams without escalating latency and memory usage. Processing entire videos with…

Computer Vision and Pattern Recognition · Computer Science 2025-10-13 Ruyi Xu , Guangxuan Xiao , Yukang Chen , Liuning He , Kelly Peng , Yao Lu , Song Han