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Trajectory data is essential for various applications as it records the movement of vehicles. However, publicly available trajectory datasets remain limited in scale due to privacy concerns, which hinders the development of trajectory data…

Machine Learning · Computer Science 2024-09-12 Tonglong Wei , Youfang Lin , Shengnan Guo , Yan Lin , Yiheng Huang , Chenyang Xiang , Yuqing Bai , Huaiyu Wan

Recent advances in generative video models have enabled the creation of high-quality videos based on natural language prompts. However, these models frequently lack fine-grained temporal control, meaning they do not allow users to specify…

Computer Vision and Pattern Recognition · Computer Science 2026-04-02 Shira Schiber , Ofir Lindenbaum , Idan Schwartz

Recent advances in diffusion-based text-to-video (T2V) models have demonstrated remarkable progress, but these models still face challenges in generating videos with multiple objects. Most models struggle with accurately capturing complex…

Computer Vision and Pattern Recognition · Computer Science 2025-05-30 Aimon Rahman , Jiang Liu , Ze Wang , Ximeng Sun , Jialian Wu , Xiaodong Yu , Yusheng Su , Vishal M. Patel , Zicheng Liu , Emad Barsoum

Tracking any point based on image frames is constrained by frame rates, leading to instability in high-speed scenarios and limited generalization in real-world applications. To overcome these limitations, we propose an image-event fusion…

Computer Vision and Pattern Recognition · Computer Science 2024-09-19 Jiaxiong Liu , Bo Wang , Zhen Tan , Jinpu Zhang , Hui Shen , Dewen Hu

With the impressive progress in diffusion-based text-to-image generation, extending such powerful generative ability to text-to-video raises enormous attention. Existing methods either require large-scale text-video pairs and a large number…

Computer Vision and Pattern Recognition · Computer Science 2023-10-18 Ruiqi Wu , Liangyu Chen , Tong Yang , Chunle Guo , Chongyi Li , Xiangyu Zhang

Current methods for trajectory prediction operate in supervised manners, and therefore require vast quantities of corresponding ground truth data for training. In this paper, we present a novel, label-free algorithm, AutoTrajectory, for…

Computer Vision and Pattern Recognition · Computer Science 2020-07-14 Yuexin Ma , Xinge ZHU , Xinjing Cheng , Ruigang Yang , Jiming Liu , Dinesh Manocha

Generating controllable character animation from a reference image and motion guidance remains a challenging task due to the inherent difficulty of injecting appearance and motion cues into video diffusion models. Prior works often rely on…

Graphics · Computer Science 2025-07-03 Guian Fang , Yuchao Gu , Mike Zheng Shou

With the rapid advancement of game and film production, generating interactive motion from texts has garnered significant attention due to its potential to revolutionize content creation processes. In many practical applications, there is a…

Robotics · Computer Science 2025-02-18 Runqi Wang , Caoyuan Ma , Jian Zhao , Hanrui Xu , Dongfang Sun , Haoyang Chen , Lin Xiong , Zheng Wang , Xuelong Li

Video generation has achieved remarkable progress with the introduction of diffusion models, which have significantly improved the quality of generated videos. However, recent research has primarily focused on scaling up model training,…

Computer Vision and Pattern Recognition · Computer Science 2025-01-16 Chenyang Si , Weichen Fan , Zhengyao Lv , Ziqi Huang , Yu Qiao , Ziwei Liu

Character Animation aims to generating character videos from still images through driving signals. Currently, diffusion models have become the mainstream in visual generation research, owing to their robust generative capabilities. However,…

Computer Vision and Pattern Recognition · Computer Science 2024-06-14 Li Hu , Xin Gao , Peng Zhang , Ke Sun , Bang Zhang , Liefeng Bo

This paper presents \emph{ControlVideo} for text-driven video editing -- generating a video that aligns with a given text while preserving the structure of the source video. Building on a pre-trained text-to-image diffusion model,…

Computer Vision and Pattern Recognition · Computer Science 2023-11-29 Min Zhao , Rongzhen Wang , Fan Bao , Chongxuan Li , Jun Zhu

In this paper, we present VideoGen, a text-to-video generation approach, which can generate a high-definition video with high frame fidelity and strong temporal consistency using reference-guided latent diffusion. We leverage an…

Computer Vision and Pattern Recognition · Computer Science 2023-09-08 Xin Li , Wenqing Chu , Ye Wu , Weihang Yuan , Fanglong Liu , Qi Zhang , Fu Li , Haocheng Feng , Errui Ding , Jingdong Wang

Advancements in diffusion models have significantly improved video quality, directing attention to fine-grained controllability. However, many existing methods depend on fine-tuning large-scale video models for specific tasks, which becomes…

Computer Vision and Pattern Recognition · Computer Science 2026-03-04 Sangwon Jang , Taekyung Ki , Jaehyeong Jo , Jaehong Yoon , Soo Ye Kim , Zhe Lin , Sung Ju Hwang

The generation of realistic and controllable GPS trajectories is a fundamental task for applications in urban planning, mobility simulation, and privacy-preserving data sharing. However, existing methods face a two-fold challenge: they lack…

Artificial Intelligence · Computer Science 2026-05-05 Yuanshao Zhu , Yuxuan Liang , Xiangyu Zhao , Liang Han , Xinwei Fang , Xun Zhou , Xuetao Wei , James Jianqiao Yu

Large-scale video generation models have the inherent ability to realistically model natural scenes. In this paper, we demonstrate that through a careful design of a generative video propagation framework, various video tasks can be…

Computer Vision and Pattern Recognition · Computer Science 2024-12-30 Shaoteng Liu , Tianyu Wang , Jui-Hsien Wang , Qing Liu , Zhifei Zhang , Joon-Young Lee , Yijun Li , Bei Yu , Zhe Lin , Soo Ye Kim , Jiaya Jia

Real-time motion-controllable video generation remains challenging due to the inherent latency of bidirectional diffusion models and the lack of effective autoregressive (AR) approaches. Existing AR video diffusion models are limited to…

Computer Vision and Pattern Recognition · Computer Science 2026-03-10 Kesen Zhao , Jiaxin Shi , Beier Zhu , Junbao Zhou , Xiaolong Shen , Yuan Zhou , Qianru Sun , Hanwang Zhang

Existing video generation models struggle to follow complex text prompts and synthesize multiple objects, raising the need for additional grounding input for improved controllability. In this work, we propose to decompose videos into visual…

Computer Vision and Pattern Recognition · Computer Science 2025-01-15 Weixi Feng , Chao Liu , Sifei Liu , William Yang Wang , Arash Vahdat , Weili Nie

Video compositing combines live-action footage to create video production, serving as a crucial technique in video creation and film production. Traditional pipelines require intensive labor efforts and expert collaboration, resulting in…

Computer Vision and Pattern Recognition · Computer Science 2026-03-20 Shuzhou Yang , Xiaoyu Li , Xiaodong Cun , Guangzhi Wang , Lingen Li , Ying Shan , Jian Zhang

Traditional methods for autonomous driving are implemented with many building blocks from perception, planning and control, making them difficult to generalize to varied scenarios due to complex assumptions and interdependencies. Recently,…

Computer Vision and Pattern Recognition · Computer Science 2020-11-03 Peide Cai , Yuxiang Sun , Hengli Wang , Ming Liu

Recent advances in the diffusion models have significantly improved text-to-image generation. However, generating videos from text is a more challenging task than generating images from text, due to the much larger dataset and higher…

Computer Vision and Pattern Recognition · Computer Science 2024-12-31 Taegyeong Lee , Soyeong Kwon , Taehwan Kim