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Current 4D generation methods have achieved noteworthy efficacy with the aid of advanced diffusion generative models. However, these methods lack multi-view spatial-temporal modeling and encounter challenges in integrating diverse prior…

Computer Vision and Pattern Recognition · Computer Science 2024-10-23 Haiyu Zhang , Xinyuan Chen , Yaohui Wang , Xihui Liu , Yunhong Wang , Yu Qiao

Recent breakthroughs in text-to-image generation has shown encouraging results via large generative models. Due to the scarcity of 3D assets, it is hardly to transfer the success of text-to-image generation to that of text-to-3D generation.…

Computer Vision and Pattern Recognition · Computer Science 2023-11-28 Yiming Chen , Zhiqi Li , Peidong Liu

We introduce V-Trans4Style, an innovative algorithm tailored for dynamic video content editing needs. It is designed to adapt videos to different production styles like documentaries, dramas, feature films, or a specific YouTube channel's…

Computer Vision and Pattern Recognition · Computer Science 2025-01-15 Pooja Guhan , Tsung-Wei Huang , Guan-Ming Su , Subhadra Gopalakrishnan , Dinesh Manocha

Generating consistent long videos is a complex challenge: while diffusion-based generative models generate visually impressive short clips, extending them to longer durations often leads to memory bottlenecks and long-term inconsistency. In…

Computer Vision and Pattern Recognition · Computer Science 2025-07-22 Wenqi Ouyang , Zeqi Xiao , Danni Yang , Yifan Zhou , Shuai Yang , Lei Yang , Jianlou Si , Xingang Pan

Text-to-video (T2V) synthesis has gained increasing attention in the community, in which the recently emerged diffusion models (DMs) have promisingly shown stronger performance than the past approaches. While existing state-of-the-art DMs…

Artificial Intelligence · Computer Science 2024-03-20 Hao Fei , Shengqiong Wu , Wei Ji , Hanwang Zhang , Tat-Seng Chua

Videos for mobile devices become the most popular access to share and acquire information recently. For the convenience of users' creation, in this paper, we present a system, namely MobileVidFactory, to automatically generate vertical…

Computer Vision and Pattern Recognition · Computer Science 2023-08-01 Junchen Zhu , Huan Yang , Wenjing Wang , Huiguo He , Zixi Tuo , Yongsheng Yu , Wen-Huang Cheng , Lianli Gao , Jingkuan Song , Jianlong Fu , Jiebo Luo

Despite the ability of existing large-scale text-to-image (T2I) models to generate high-quality images from detailed textual descriptions, they often lack the ability to precisely edit the generated or real images. In this paper, we propose…

Computer Vision and Pattern Recognition · Computer Science 2023-11-21 Chong Mou , Xintao Wang , Jiechong Song , Ying Shan , Jian Zhang

We present Dual3D, a novel text-to-3D generation framework that generates high-quality 3D assets from texts in only $1$ minute.The key component is a dual-mode multi-view latent diffusion model. Given the noisy multi-view latents, the 2D…

Computer Vision and Pattern Recognition · Computer Science 2024-05-17 Xinyang Li , Zhangyu Lai , Linning Xu , Jianfei Guo , Liujuan Cao , Shengchuan Zhang , Bo Dai , Rongrong Ji

Video generation has been advancing rapidly, and diffusion transformer (DiT) based models have demonstrated remark- able capabilities. However, their practical deployment is of- ten hindered by slow inference speeds and high memory con-…

Computer Vision and Pattern Recognition · Computer Science 2025-11-18 Sijie Wang , Qiang Wang , Shaohuai Shi

Recent advances in video generation have made it possible to produce visually compelling videos, with wide-ranging applications in content creation, entertainment, and virtual reality. However, most existing diffusion transformer based…

Computer Vision and Pattern Recognition · Computer Science 2025-10-22 Teng Hu , Jiangning Zhang , Zihan Su , Ran Yi

Generating temporally-consistent high-fidelity videos can be computationally expensive, especially over longer temporal spans. More-recent Diffusion Transformers (DiTs) -- despite making significant headway in this context -- have only…

Computer Vision and Pattern Recognition · Computer Science 2024-11-08 Kumara Kahatapitiya , Haozhe Liu , Sen He , Ding Liu , Menglin Jia , Chenyang Zhang , Michael S. Ryoo , Tian Xie

A diffusion probabilistic model (DPM), which constructs a forward diffusion process by gradually adding noise to data points and learns the reverse denoising process to generate new samples, has been shown to handle complex data…

Computer Vision and Pattern Recognition · Computer Science 2023-10-16 Zhengxiong Luo , Dayou Chen , Yingya Zhang , Yan Huang , Liang Wang , Yujun Shen , Deli Zhao , Jingren Zhou , Tieniu Tan

Recently, advancements in video synthesis have attracted significant attention. Video synthesis models such as AnimateDiff and Stable Video Diffusion have demonstrated the practical applicability of diffusion models in creating dynamic…

Computer Vision and Pattern Recognition · Computer Science 2024-06-21 Zhongjie Duan , Wenmeng Zhou , Cen Chen , Yaliang Li , Weining Qian

Diffusion models have demonstrated great success in text-to-video (T2V) generation. However, existing methods may face challenges when handling complex (long) video generation scenarios that involve multiple objects or dynamic changes in…

Computer Vision and Pattern Recognition · Computer Science 2024-10-15 Ye Tian , Ling Yang , Haotian Yang , Yuan Gao , Yufan Deng , Jingmin Chen , Xintao Wang , Zhaochen Yu , Xin Tao , Pengfei Wan , Di Zhang , Bin Cui

Text-to-image (T2I) models are well known for their ability to produce highly realistic images, while multimodal large language models (MLLMs) are renowned for their proficiency in understanding and integrating multiple modalities. However,…

Computer Vision and Pattern Recognition · Computer Science 2025-08-12 Jian Ma , Qirong Peng , Xu Guo , Chen Chen , Haonan Lu , Zhenyu Yang

Visual tokenizers map high-dimensional raw pixels into a compressed representation for downstream modeling. Beyond compression, tokenizers dictate what information is preserved and how it is organized. A de facto standard approach to video…

Computer Vision and Pattern Recognition · Computer Science 2026-04-15 Andrei Atanov , Jesse Allardice , Roman Bachmann , Oğuzhan Fatih Kar , R Devon Hjelm , David Griffiths , Peter Fu , Afshin Dehghan , Amir Zamir

Rapid advancements in video diffusion models have enabled the creation of realistic videos, raising concerns about unauthorized use and driving the demand for techniques to protect model ownership. Existing watermarking methods, while…

Computer Vision and Pattern Recognition · Computer Science 2025-03-31 MinHyuk Jang , Youngdong Jang , JaeHyeok Lee , Feng Yang , Gyeongrok Oh , Jongheon Jeong , Sangpil Kim

In this paper, we study Text-to-3D content generation leveraging 2D diffusion priors to enhance the quality and detail of the generated 3D models. Recent progress (Magic3D) in text-to-3D has shown that employing high-resolution (e.g., 512 x…

Computer Vision and Pattern Recognition · Computer Science 2023-08-01 Jinbo Wu , Xiaobo Gao , Xing Liu , Zhengyang Shen , Chen Zhao , Haocheng Feng , Jingtuo Liu , Errui Ding

Diffusion transformers have demonstrated remarkable generation quality, albeit requiring longer training iterations and numerous inference steps. In each denoising step, diffusion transformers encode the noisy inputs to extract the…

Computer Vision and Pattern Recognition · Computer Science 2025-04-10 Shuai Wang , Zhi Tian , Weilin Huang , Limin Wang