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Related papers: Toward Physically Consistent Driving Video World M…

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Closed-loop simulation and scalable pre-training for autonomous driving require synthesizing free-viewpoint driving scenes. However, existing datasets and generative pipelines rarely provide consistent off-trajectory observations, limiting…

Computer Vision and Pattern Recognition · Computer Science 2025-12-05 Shijie Chen , Peixi Peng

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…

Existing video generation models predominantly emphasize appearance fidelity while exhibiting limited ability to synthesize complex human motions, such as whole-body movements, long-range dynamics, and fine-grained human-environment…

Computer Vision and Pattern Recognition · Computer Science 2026-02-25 Haoyu Wang , Hao Tang , Donglin Di , Zhilu Zhang , Wangmeng Zuo , Feng Gao , Siwei Ma , Shiliang Zhang

End-to-end autonomous driving aims to generate safe and plausible planning policies from raw sensor input. Driving world models have shown great potential in learning rich representations by predicting the future evolution of a driving…

Computer Vision and Pattern Recognition · Computer Science 2026-03-17 Xingtai Gui , Meijie Zhang , Tianyi Yan , Wencheng Han , Jiahao Gong , Feiyang Tan , Cheng-zhong Xu , Jianbing Shen

Driving world models are used to simulate futures by video generation based on the condition of the current state and actions. However, current models often suffer serious error accumulations when predicting the long-term future, which…

Computer Vision and Pattern Recognition · Computer Science 2025-06-03 Xiaodong Wang , Zhirong Wu , Peixi Peng

Generating realistic human motion is a central yet unsolved challenge in video generation. While reinforcement learning (RL)-based post-training has driven recent gains in general video quality, extending it to human motion remains…

Computer Vision and Pattern Recognition · Computer Science 2026-05-15 Yidong Huang , Zun Wang , Han Lin , Dong-Ki Kim , Shayegan Omidshafiei , Jaehong Yoon , Jaemin Cho , Yue Zhang , Mohit Bansal

Advances in technology have led to the development of methods that can create desired visual multimedia. In particular, image generation using deep learning has been extensively studied across diverse fields. In comparison, video…

Computer Vision and Pattern Recognition · Computer Science 2021-06-29 Doyeon Kim , Donggyu Joo , Junmo Kim

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

Creating high-dynamic videos such as motion-rich actions and sophisticated visual effects poses a significant challenge in the field of artificial intelligence. Unfortunately, current state-of-the-art video generation methods, primarily…

Computer Vision and Pattern Recognition · Computer Science 2023-11-21 Yan Zeng , Guoqiang Wei , Jiani Zheng , Jiaxin Zou , Yang Wei , Yuchen Zhang , Hang Li

Vision-and-Language Navigation (VLN) is a challenging task that requires an agent to navigate through photorealistic environments following natural-language instructions. One main obstacle existing in VLN is data scarcity, leading to poor…

Computer Vision and Pattern Recognition · Computer Science 2025-06-26 Yu Zhong , Rui Zhang , Zihao Zhang , Shuo Wang , Chuan Fang , Xishan Zhang , Jiaming Guo , Shaohui Peng , Di Huang , Yanyang Yan , Xing Hu , Qi Guo

Despite impressive progress in video generation, existing models remain limited to surface-level plausibility, lacking a coherent and unified understanding of the world. Prior approaches typically incorporate only a single form of…

Computer Vision and Pattern Recognition · Computer Science 2026-03-03 Boming Tan , Xiangdong Zhang , Ning Liao , Yuqing Zhang , Shaofeng Zhang , Xue Yang , Qi Fan , Yanyong Zhang

OpenAI's Sora highlights the potential of video generation for developing world models that adhere to fundamental physical laws. However, the ability of video generation models to discover such laws purely from visual data without human…

Computer Vision and Pattern Recognition · Computer Science 2025-06-24 Bingyi Kang , Yang Yue , Rui Lu , Zhijie Lin , Yang Zhao , Kaixin Wang , Gao Huang , Jiashi Feng

Image and video synthesis are closely related areas aiming at generating content from noise. While rapid progress has been demonstrated in improving image-based models to handle large resolutions, high-quality renderings, and wide…

Computer Vision and Pattern Recognition · Computer Science 2021-05-03 Yu Tian , Jian Ren , Menglei Chai , Kyle Olszewski , Xi Peng , Dimitris N. Metaxas , Sergey Tulyakov

Long-form video generation presents a dual challenge: models must capture long-range dependencies while preventing the error accumulation inherent in autoregressive decoding. To address these challenges, we make two contributions. First,…

Computer Vision and Pattern Recognition · Computer Science 2025-10-06 Xiaofei Wu , Guozhen Zhang , Zhiyong Xu , Yuan Zhou , Qinglin Lu , Xuming He

Autonomous driving promises safer roads, reduced congestion, and improved mobility, yet validating these systems across diverse conditions remains a major challenge. Real-world testing is expensive, time-consuming, and sometimes unsafe,…

Physics-aware driving world model is essential for drive planning, out-of-distribution data synthesis, and closed-loop evaluation. However, existing methods often rely on a single diffusion model to directly map driving actions to videos,…

Computer Vision and Pattern Recognition · Computer Science 2025-12-16 Zhenya Yang , Zhe Liu , Yuxiang Lu , Liping Hou , Chenxuan Miao , Siyi Peng , Bailan Feng , Xiang Bai , Hengshuang Zhao

Synthesizing motion-rich and temporally consistent videos remains a challenge in artificial intelligence, especially when dealing with extended durations. Existing text-to-video (T2V) models commonly employ spatial cross-attention for text…

Computer Vision and Pattern Recognition · Computer Science 2025-08-18 Jiasong Feng , Ao Ma , Jing Wang , Ke Cao , Zhanjie Zhang

Generating videos for visual storytelling can be a tedious and complex process that typically requires either live-action filming or graphics animation rendering. To bypass these challenges, our key idea is to utilize the abundance of…

Computer Vision and Pattern Recognition · Computer Science 2023-07-14 Yingqing He , Menghan Xia , Haoxin Chen , Xiaodong Cun , Yuan Gong , Jinbo Xing , Yong Zhang , Xintao Wang , Chao Weng , Ying Shan , Qifeng Chen

Recent advances in world models have greatly enhanced interactive environment simulation. Existing methods mainly fall into two categories: (1) static world generation models, which construct 3D environments without active agents, and (2)…

Computer Vision and Pattern Recognition · Computer Science 2025-12-22 Yitong Wang , Fangyun Wei , Hongyang Zhang , Bo Dai , Yan Lu

Simulation is an indispensable tool in the development and testing of autonomous vehicles (AVs), offering an efficient and safe alternative to road testing. An outstanding challenge with simulation-based testing is the generation of…

Robotics · Computer Science 2024-12-13 Peide Huang , Wenhao Ding , Benjamin Stoler , Jonathan Francis , Bingqing Chen , Ding Zhao