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In this paper, we introduce \textit{DecoRec}, a novel system designed to elevate single-view 2D images to a decomposed 3D scene mesh. Current methods for single-view scene reconstruction typically rely on object retrieval or the regression…

Computer Vision and Pattern Recognition · Computer Science 2026-05-19 Yuhan Ping , Yuan Liu , Xiaoxiao Long , Peng Wang , Junhui Hou , Jianyi Zheng , Jia Pan , Xin Li , Cheng Lin

We introduce MapAnything, a unified transformer-based feed-forward model that ingests one or more images along with optional geometric inputs such as camera intrinsics, poses, depth, or partial reconstructions, and then directly regresses…

We present the first real-time human performance capture approach that reconstructs dense, space-time coherent deforming geometry of entire humans in general everyday clothing from just a single RGB video. We propose a novel two-stage…

Computer Vision and Pattern Recognition · Computer Science 2019-01-28 Marc Habermann , Weipeng Xu , Michael Zollhoefer , Gerard Pons-Moll , Christian Theobalt

We address the problem of dynamic scene reconstruction from sparse-view videos. Prior work often requires dense multi-view captures with hundreds of calibrated cameras (e.g. Panoptic Studio). Such multi-view setups are prohibitively…

Computer Vision and Pattern Recognition · Computer Science 2026-03-03 Zihan Wang , Jeff Tan , Tarasha Khurana , Neehar Peri , Deva Ramanan

We present SS4D, a native 4D generative model that synthesizes dynamic 3D objects directly from monocular video. Unlike prior approaches that construct 4D representations by optimizing over 3D or video generative models, we train a…

Computer Vision and Pattern Recognition · Computer Science 2025-12-17 Zhibing Li , Mengchen Zhang , Tong Wu , Jing Tan , Jiaqi Wang , Dahua Lin

Building animatable 3D models is challenging due to the need for 3D scans, laborious registration, and manual rigging, which are difficult to scale to arbitrary categories. Recently, differentiable rendering provides a pathway to obtain…

Computer Vision and Pattern Recognition · Computer Science 2023-05-11 Gengshan Yang , Chaoyang Wang , N Dinesh Reddy , Deva Ramanan

Learning to understand dynamic 3D scenes from imagery is crucial for applications ranging from robotics to scene reconstruction. Yet, unlike other problems where large-scale supervised training has enabled rapid progress, directly…

Computer Vision and Pattern Recognition · Computer Science 2025-05-01 Linyi Jin , Richard Tucker , Zhengqi Li , David Fouhey , Noah Snavely , Aleksander Holynski

Foundation models pre-trained on massive unlabeled datasets have revolutionized natural language and computer vision, exhibiting remarkable generalization capabilities, thus highlighting the importance of pre-training. Yet, efforts in…

Robotics · Computer Science 2025-05-20 Dantong Niu , Yuvan Sharma , Haoru Xue , Giscard Biamby , Junyi Zhang , Ziteng Ji , Trevor Darrell , Roei Herzig

Reconstructing dynamic assets from video data is central to many in computer vision and graphics tasks. Existing 4D reconstruction approaches are limited by category-specific models or slow optimization-based methods. Inspired by the recent…

Computer Vision and Pattern Recognition · Computer Science 2025-03-31 Remy Sabathier , Niloy J. Mitra , David Novotny

Novel view synthesis of dynamic scenes is becoming important in various applications, including augmented and virtual reality. We propose a novel 4D Gaussian Splatting (4DGS) algorithm for dynamic scenes from casually recorded monocular…

Computer Vision and Pattern Recognition · Computer Science 2024-11-14 Mijeong Kim , Jongwoo Lim , Bohyung Han

Recovering a dynamic 3D scene from a long monocular video is crucial for dense geometry, camera motion, and temporal correspondence to remain consistent in a shared coordinate system. Existing methods face two key challenges: (1)…

Computer Vision and Pattern Recognition · Computer Science 2026-05-19 Chenyi Xu , Yihao Wu , Liqi Yan , Chao Yang , Jianhui Zhang , Fangli Guan , Pan Li

Recent advancements in foundation models for 2D vision have substantially improved the analysis of dynamic scenes from monocular videos. However, despite their strong generalization capabilities, these models often lack 3D consistency, a…

Computer Vision and Pattern Recognition · Computer Science 2025-12-04 Haoran Zhou , Gim Hee Lee

We present a novel approach for the reconstruction of dynamic geometric shapes using a single hand-held consumer-grade RGB-D sensor at real-time rates. Our method does not require a pre-defined shape template to start with and builds up the…

Computer Vision and Pattern Recognition · Computer Science 2016-08-02 Matthias Innmann , Michael Zollhöfer , Matthias Nießner , Christian Theobalt , Marc Stamminger

Dynamic urban environments are often captured by cameras placed at spatially separated locations with little or no view overlap. However, most existing 4D reconstruction methods assume densely overlapping views. When applied to such sparse…

Computer Vision and Pattern Recognition · Computer Science 2026-04-10 Hina Kogure , Kei Katsumata , Taiki Miyanishi , Komei Sugiura

Monocular Semantic Occupancy Prediction aims to infer the complete 3D geometry and semantic information of scenes from only 2D images. It has garnered significant attention, particularly due to its potential to enhance the 3D perception of…

Computer Vision and Pattern Recognition · Computer Science 2024-03-14 Yupeng Zheng , Xiang Li , Pengfei Li , Yuhang Zheng , Bu Jin , Chengliang Zhong , Xiaoxiao Long , Hao Zhao , Qichao Zhang

Reconstructing non-rigid objects with physical plausibility remains a significant challenge. Existing approaches leverage differentiable rendering for per-scene optimization, recovering geometry and dynamics but requiring expensive tuning…

Computer Vision and Pattern Recognition · Computer Science 2026-04-10 Boyuan Wang , Xiaofeng Wang , Yongkang Li , Zheng Zhu , Yifan Chang , Angen Ye , Guosheng Zhao , Chaojun Ni , Guan Huang , Yijie Ren , Yueqi Duan , Xingang Wang

Current 4D representations decouple geometry, motion, and semantics: reconstruction methods discard interpretable motion structure; language-grounded methods attach semantics after motion is learned, blind to how objects move; and…

Computer Vision and Pattern Recognition · Computer Science 2026-03-17 Mohamed Rayan Barhdadi , Samir Abdaljalil , Rasul Khanbayov , Erchin Serpedin , Hasan Kurban

Colorectal cancer screening critically depends on colonoscopy, yet existing platforms offer limited support for systematically studying the coupled dynamics of operator control, instrument motion, and visual feedback. This gap restricts…

We present a method to capture temporally coherent dynamic clothing deformation from a monocular RGB video input. In contrast to the existing literature, our method does not require a pre-scanned personalized mesh template, and thus can be…

Computer Vision and Pattern Recognition · Computer Science 2020-11-24 Donglai Xiang , Fabian Prada , Chenglei Wu , Jessica Hodgins

Recent 4D reconstruction methods have yielded impressive results but rely on sharp videos as supervision. However, motion blur often occurs in videos due to camera shake and object movement, while existing methods render blurry results when…

Computer Vision and Pattern Recognition · Computer Science 2026-01-21 Renlong Wu , Zhilu Zhang , Mingyang Chen , Zifei Yan , Wangmeng Zuo
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