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While recent video diffusion models (VDMs) produce visually impressive results, they fundamentally struggle to maintain 3D structural consistency, often resulting in object deformation or spatial drift. We hypothesize that these failures…

Computer Vision and Pattern Recognition · Computer Science 2026-05-13 Hongyang Du , Junjie Ye , Xiaoyan Cong , Runhao Li , Jingcheng Ni , Aman Agarwal , Zeqi Zhou , Zekun Li , Randall Balestriero , Yue Wang

Text- and image-conditioned video generation models have achieved strong visual fidelity and temporal coherence, but they often fail to generate motion governed by kinematic and geometric constraints. In these settings, object parts must…

Computer Vision and Pattern Recognition · Computer Science 2026-05-15 Rahul Jain , Mayank Patel , Asim Unmesh , Karthik Ramani

Text-to-3D (T23D) generation has emerged as a crucial visual generation task, aiming at synthesizing 3D content from textual descriptions. Studies of this task are currently shifting from per-scene T23D, which requires optimization of the…

Computer Vision and Pattern Recognition · Computer Science 2025-12-04 Xiao Cai , Sitong Su , Jingkuan Song , Pengpeng Zeng , Ji Zhang , Qinhong Du , Mengqi Li , Heng Tao Shen , Lianli Gao

Video generation models have achieved remarkable progress in creating high-quality, photorealistic content. However, their ability to accurately simulate physical phenomena remains a critical and unresolved challenge. This paper presents…

Computer Vision and Pattern Recognition · Computer Science 2026-05-27 Jing Gu , Xian Liu , Yu Zeng , Ashwin Nagarajan , Fangrui Zhu , Daniel Hong , Yue Fan , Qianqi Yan , Kaiwen Zhou , Ming-Yu Liu , Xin Eric Wang

Reconstructing dynamic 3D scenes from sparse multi-view videos is highly ill-posed, often leading to geometric collapse, trajectory drift, and floating artifacts. Recent attempts introduce generative priors to hallucinate missing content,…

Computer Vision and Pattern Recognition · Computer Science 2026-04-23 Zhenlong Wu , Zihan Zheng , Xuanxuan Wang , Qianhe Wang , Hua Yang , Xiaoyun Zhang , Qiang Hu , Wenjun Zhang

Recovering editable CAD programs from images or 3D observations is central to AI-assisted design, but progress is difficult to measure because existing evaluations are fragmented across datasets, modalities, and metrics. We introduce…

Computer Vision and Pattern Recognition · Computer Science 2026-05-12 Anna C. Doris , Jacob Thomas Sony , Ghadi Nehme , Era Syla , Amin Heyrani Nobari , Faez Ahmed

Predominant techniques on talking head generation largely depend on 2D information, including facial appearances and motions from input face images. Nevertheless, dense 3D facial geometry, such as pixel-wise depth, plays a critical role in…

Computer Vision and Pattern Recognition · Computer Science 2023-12-12 Fa-Ting Hong , Li Shen , Dan Xu

We present a video generation model that accurately reproduces object motion, changes in camera viewpoint, and new content that arises over time. Existing video generation methods often fail to produce new content as a function of time…

Computer Vision and Pattern Recognition · Computer Science 2022-06-10 Tim Brooks , Janne Hellsten , Miika Aittala , Ting-Chun Wang , Timo Aila , Jaakko Lehtinen , Ming-Yu Liu , Alexei A. Efros , Tero Karras

Video Instance Segmentation (VIS) fundamentally struggles with pervasive challenges including object occlusions, motion blur, and appearance variations during temporal association. To overcome these limitations, this work introduces…

Computer Vision and Pattern Recognition · Computer Science 2025-07-15 Quanzhu Niu , Yikang Zhou , Shihao Chen , Tao Zhang , Shunping Ji

In this paper, we propose VideoFrom3D, a novel framework for synthesizing high-quality 3D scene videos from coarse geometry, a camera trajectory, and a reference image. Our approach streamlines the 3D graphic design workflow, enabling…

Graphics · Computer Science 2025-09-23 Geonung Kim , Janghyeok Han , Sunghyun Cho

We present GEN3C, a generative video model with precise Camera Control and temporal 3D Consistency. Prior video models already generate realistic videos, but they tend to leverage little 3D information, leading to inconsistencies, such as…

Computer Vision and Pattern Recognition · Computer Science 2025-03-06 Xuanchi Ren , Tianchang Shen , Jiahui Huang , Huan Ling , Yifan Lu , Merlin Nimier-David , Thomas Müller , Alexander Keller , Sanja Fidler , Jun Gao

Recent advances in internet-scale video data pretraining have led to the development of text-to-video generative models that can create high-quality videos across a broad range of visual concepts, synthesize realistic motions and render…

Computer Vision and Pattern Recognition · Computer Science 2024-10-04 Hritik Bansal , Zongyu Lin , Tianyi Xie , Zeshun Zong , Michal Yarom , Yonatan Bitton , Chenfanfu Jiang , Yizhou Sun , Kai-Wei Chang , Aditya Grover

3D object detection is an important capability needed in various practical applications such as driver assistance systems. Monocular 3D detection, as a representative general setting among image-based approaches, provides a more economical…

Computer Vision and Pattern Recognition · Computer Science 2021-11-29 Tai Wang , Xinge Zhu , Jiangmiao Pang , Dahua Lin

Recent advancements in video generation have substantially improved visual quality and temporal coherence, making these models increasingly appealing for applications such as autonomous driving, particularly in the context of driving…

Computer Vision and Pattern Recognition · Computer Science 2025-08-25 Chun-Peng Chang , Chen-Yu Wang , Julian Schmidt , Holger Caesar , Alain Pagani

4D video control is essential in video generation as it enables the use of sophisticated lens techniques, such as multi-camera shooting and dolly zoom, which are currently unsupported by existing methods. Training a video Diffusion…

Computer Vision and Pattern Recognition · Computer Science 2025-01-07 Weikang Bian , Zhaoyang Huang , Xiaoyu Shi , Yijin Li , Fu-Yun Wang , Hongsheng Li

Recent video foundation models demonstrate impressive visual synthesis but frequently suffer from geometric inconsistencies. While existing methods attempt to inject 3D priors via architectural modifications, they often incur high…

Computer Vision and Pattern Recognition · Computer Science 2026-05-27 Weijie Wang , Xiaoxuan He , Youping Gu , Yifan Yang , Zeyu Zhang , Yefei He , Yanbo Ding , Xirui Hu , Donny Y. Chen , Zhiyuan He , Yuqing Yang , Bohan Zhuang

Interactive world models are advancing rapidly, yet existing benchmarks cover only part of the required competencies, leaving no unified standard for systematic evaluation. To fill this gap, we introduce WBench, a comprehensive multi-turn…

Computer Vision and Pattern Recognition · Computer Science 2026-05-26 Kaining Ying , Hengrui Hu , Siyu Ren , Jiamu Li , Fengjiao Chen , Ziwen Wang , Xuezhi Cao , Xunliang Cai , Henghui Ding

Today's driving world models can generate remarkably realistic dash-cam videos, yet no single model excels universally. Some generate photorealistic textures but violate basic physics; others maintain geometric consistency but fail when…

Text-to-video generative models have made significant strides in recent years, producing high-quality videos that excel in both aesthetic appeal and accurate instruction following, and have become central to digital art creation and user…

Machine Learning · Computer Science 2025-05-02 Xuyang Guo , Jiayan Huo , Zhenmei Shi , Zhao Song , Jiahao Zhang , Jiale Zhao

Human perception for effective object tracking in 2D video streams arises from the implicit use of prior 3D knowledge and semantic reasoning. In contrast, most generic object tracking (GOT) methods primarily rely on 2D features of the…

Computer Vision and Pattern Recognition · Computer Science 2026-02-25 Shih-Fang Chen , Jun-Cheng Chen , I-Hong Jhuo , Yen-Yu Lin
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