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Recent video generation models have achieved remarkable progress and are now deployed in film, social media production, and advertising. Beyond their creative potential, such models also hold promise as world simulators for robotics and…

Computer Vision and Pattern Recognition · Computer Science 2026-03-24 David Romero , Ariana Bermudez , Viacheslav Iablochnikov , Hao Li , Fabio Pizzati , Ivan Laptev

Generating realistic 3D human-object interactions (HOIs) remains a challenging task due to the difficulty of modeling detailed interaction dynamics. Existing methods treat human and object motions independently, resulting in physically…

Computer Vision and Pattern Recognition · Computer Science 2025-10-21 Lin Wu , Zhixiang Chen , Jianglin Lan

In the deep learning era, long video generation of high-quality still remains challenging due to the spatio-temporal complexity and continuity of videos. Existing prior works have attempted to model video distribution by representing videos…

Computer Vision and Pattern Recognition · Computer Science 2022-02-23 Sihyun Yu , Jihoon Tack , Sangwoo Mo , Hyunsu Kim , Junho Kim , Jung-Woo Ha , Jinwoo Shin

We present Interleaved Learning for Motion Synthesis (InterSyn), a novel framework that targets the generation of realistic interaction motions by learning from integrated motions that consider both solo and multi-person dynamics. Unlike…

Computer Vision and Pattern Recognition · Computer Science 2025-08-15 Yiyi Ma , Yuanzhi Liang , Xiu Li , Chi Zhang , Xuelong Li

Diffusion models have emerged as a powerful generative method for synthesizing high-quality and diverse set of images. In this paper, we propose a video generation method based on diffusion models, where the effects of motion are modeled in…

Computer Vision and Pattern Recognition · Computer Science 2022-12-02 Kangfu Mei , Vishal M. Patel

World models, which predict future transitions from past observation and action sequences, have shown great promise for improving data efficiency in sequential decision-making. However, existing world models often require extensive…

Computer Vision and Pattern Recognition · Computer Science 2026-03-10 Siqiao Huang , Jialong Wu , Qixing Zhou , Shangchen Miao , Mingsheng Long

Despite progress in speech-to-video synthesis, existing methods often struggle to capture cross-individual dependencies and provide fine-grained control over reactive behaviors in dyadic settings. To address these challenges, we propose…

Computer Vision and Pattern Recognition · Computer Science 2026-03-25 Dongwei Pan , Longwei Guo , Jiazhi Guan , Luying Huang , Yiding Li , Haojie Liu , Haocheng Feng , Wei He , Kaisiyuan Wang , Hang Zhou

From just a glance, humans can make rich predictions about the future state of a wide range of physical systems. On the other hand, modern approaches from engineering, robotics, and graphics are often restricted to narrow domains and…

Computer Vision and Pattern Recognition · Computer Science 2017-06-06 Nicholas Watters , Andrea Tacchetti , Theophane Weber , Razvan Pascanu , Peter Battaglia , Daniel Zoran

Cinematic video production requires control over scene-subject composition and camera movement, but live-action shooting remains costly due to the need for constructing physical sets. To address this, we introduce the task of cinematic…

Computer Vision and Pattern Recognition · Computer Science 2026-02-09 Kaiyi Huang , Yukun Huang , Yu Li , Jianhong Bai , Xintao Wang , Zinan Lin , Xuefei Ning , Jiwen Yu , Pengfei Wan , Yu Wang , Xihui Liu

Generating realistic and interactive dynamics of traffic participants according to specific instruction is critical for street scene simulation. However, there is currently a lack of a comprehensive method that generates realistic dynamics…

Computer Vision and Pattern Recognition · Computer Science 2024-12-13 Yuxi Wei , Jingbo Wang , Yuwen Du , Dingju Wang , Liang Pan , Chenxin Xu , Yao Feng , Bo Dai , Siheng Chen

The landscape of video generation is shifting, from a focus on generating visually appealing clips to building virtual environments that support interaction and maintain physical plausibility. These developments point toward the emergence…

Artificial Intelligence · Computer Science 2026-02-09 Jingtong Yue , Ziqi Huang , Zhaoxi Chen , Xintao Wang , Pengfei Wan , Ziwei Liu

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

We introduce $\textit{InteractiveVideo}$, a user-centric framework for video generation. Different from traditional generative approaches that operate based on user-provided images or text, our framework is designed for dynamic interaction,…

Computer Vision and Pattern Recognition · Computer Science 2024-02-06 Yiyuan Zhang , Yuhao Kang , Zhixin Zhang , Xiaohan Ding , Sanyuan Zhao , Xiangyu Yue

Despite remarkable achievements in video synthesis, achieving granular control over complex dynamics, such as nuanced movement among multiple interacting objects, still presents a significant hurdle for dynamic world modeling, compounded by…

Computer Vision and Pattern Recognition · Computer Science 2024-03-21 Pengxiang Li , Kai Chen , Zhili Liu , Ruiyuan Gao , Lanqing Hong , Guo Zhou , Hua Yao , Dit-Yan Yeung , Huchuan Lu , Xu Jia

We have recently seen tremendous progress in diffusion advances for generating realistic human motions. Yet, they largely disregard the multi-human interactions. In this paper, we present InterGen, an effective diffusion-based approach that…

Computer Vision and Pattern Recognition · Computer Science 2024-03-29 Han Liang , Wenqian Zhang , Wenxuan Li , Jingyi Yu , Lan Xu

Recent advances in generative video modeling, driven by large-scale datasets and powerful architectures, have yielded remarkable visual realism. However, emerging evidence suggests that simply scaling data and model size does not endow…

Computer Vision and Pattern Recognition · Computer Science 2026-05-20 Ying Shen , Jerry Xiong , Tianjiao Yu , Ismini Lourentzou

Humans perform a variety of interactive motions, among which duet dance is one of the most challenging interactions. However, in terms of human motion generative models, existing works are still unable to generate high-quality interactive…

Computer Vision and Pattern Recognition · Computer Science 2024-12-24 Ronghui Li , Youliang Zhang , Yachao Zhang , Yuxiang Zhang , Mingyang Su , Jie Guo , Ziwei Liu , Yebin Liu , Xiu Li

Video inbetweening creates smooth and natural transitions between two image frames, making it an indispensable tool for video editing and long-form video synthesis. Existing works in this domain are unable to generate large, complex, or…

Computer Vision and Pattern Recognition · Computer Science 2025-10-14 Maham Tanveer , Yang Zhou , Simon Niklaus , Ali Mahdavi Amiri , Hao Zhang , Krishna Kumar Singh , Nanxuan Zhao

Motion in-betweening, a fundamental task in character animation, consists of generating motion sequences that plausibly interpolate user-provided keyframe constraints. It has long been recognized as a labor-intensive and challenging…

Computer Vision and Pattern Recognition · Computer Science 2024-05-27 Setareh Cohan , Guy Tevet , Daniele Reda , Xue Bin Peng , Michiel van de Panne

Motion control is crucial for generating expressive and compelling video content; however, most existing video generation models rely mainly on text prompts for control, which struggle to capture the nuances of dynamic actions and temporal…

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