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The blooming of virtual reality and augmented reality (VR/AR) technologies has driven an increasing demand for the creation of high-quality, immersive, and dynamic environments. However, existing generative techniques either focus solely on…

Computer Vision and Pattern Recognition · Computer Science 2024-10-04 Renjie Li , Panwang Pan , Bangbang Yang , Dejia Xu , Shijie Zhou , Xuanyang Zhang , Zeming Li , Achuta Kadambi , Zhangyang Wang , Zhengzhong Tu , Zhiwen Fan

Understanding 3D scenes in open-world settings poses fundamental challenges for vision and robotics, particularly due to the limitations of closed-vocabulary supervision and static annotations. To address this, we propose a unified…

Computer Vision and Pattern Recognition · Computer Science 2025-11-11 Fei Yu , Quan Deng , Shengeng Tang , Yuehua Li , Lechao Cheng

World models have made significant progress in modeling dynamic environments; however, most embodied world models are still restricted to 2D representations, lacking the comprehensive multi-view information essential for embodied spatial…

Computer Vision and Pattern Recognition · Computer Science 2026-05-05 Peiyan Tu , Hanxin Zhu , Jingwen Sun , Shaojie Ren , Cong Wang , Jiayi Luo , Xiaoqian Cheng , Zhibo Chen

We present Stable Video 4D 2.0 (SV4D 2.0), a multi-view video diffusion model for dynamic 3D asset generation. Compared to its predecessor SV4D, SV4D 2.0 is more robust to occlusions and large motion, generalizes better to real-world…

Computer Vision and Pattern Recognition · Computer Science 2025-03-26 Chun-Han Yao , Yiming Xie , Vikram Voleti , Huaizu Jiang , Varun Jampani

Modeling dynamic 3D scenes is challenging due to their high-dimensional nature, which requires aggregating information from multiple views to reconstruct time-evolving 3D geometry and motion. We present a novel multi-video 4D Gaussian…

Computer Vision and Pattern Recognition · Computer Science 2025-12-05 Yonghan Lee , Tsung-Wei Huang , Shiv Gehlot , Jaehoon Choi , Guan-Ming Su , Dinesh Manocha

Learning 4D language fields to enable time-sensitive, open-ended language queries in dynamic scenes is essential for many real-world applications. While LangSplat successfully grounds CLIP features into 3D Gaussian representations,…

Computer Vision and Pattern Recognition · Computer Science 2025-04-02 Wanhua Li , Renping Zhou , Jiawei Zhou , Yingwei Song , Johannes Herter , Minghan Qin , Gao Huang , Hanspeter Pfister

Creating high-quality and interactive virtual environments, such as games and simulators, often involves complex and costly manual modeling processes. In this paper, we present Video2Game, a novel approach that automatically converts videos…

Computer Vision and Pattern Recognition · Computer Science 2024-04-16 Hongchi Xia , Zhi-Hao Lin , Wei-Chiu Ma , Shenlong Wang

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 consider the problem of novel-view synthesis (NVS) for dynamic scenes. Recent neural approaches have accomplished exceptional NVS results for static 3D scenes, but extensions to 4D time-varying scenes remain non-trivial. Prior efforts…

Computer Vision and Pattern Recognition · Computer Science 2024-07-03 Yuanxing Duan , Fangyin Wei , Qiyu Dai , Yuhang He , Wenzheng Chen , Baoquan Chen

Envisioning physically plausible outcomes from a single image requires a deep understanding of the world's dynamics. To address this, we introduce PhysGen3D, a novel framework that transforms a single image into an amodal, camera-centric,…

Computer Vision and Pattern Recognition · Computer Science 2025-03-27 Boyuan Chen , Hanxiao Jiang , Shaowei Liu , Saurabh Gupta , Yunzhu Li , Hao Zhao , Shenlong Wang

Instruction-guided generative models, especially those using text-to-image (T2I) and text-to-video (T2V) diffusion frameworks, have advanced the field of content editing in recent years. To extend these capabilities to 4D scene, we…

Computer Vision and Pattern Recognition · Computer Science 2025-04-02 Hasan Iqbal , Nazmul Karim , Umar Khalid , Azib Farooq , Zichun Zhong , Chen Chen , Jing Hua

Existing dynamic scene generation methods mostly rely on distilling knowledge from pre-trained 3D generative models, which are typically fine-tuned on synthetic object datasets. As a result, the generated scenes are often object-centric and…

Computer Vision and Pattern Recognition · Computer Science 2024-11-22 Heng Yu , Chaoyang Wang , Peiye Zhuang , Willi Menapace , Aliaksandr Siarohin , Junli Cao , Laszlo A Jeni , Sergey Tulyakov , Hsin-Ying Lee

Recent advances in 4D generation mainly focus on generating 4D content by distilling pre-trained text or single-view image-conditioned models. It is inconvenient for them to take advantage of various off-the-shelf 3D assets with multi-view…

Computer Vision and Pattern Recognition · Computer Science 2024-09-10 Yanqin Jiang , Chaohui Yu , Chenjie Cao , Fan Wang , Weiming Hu , Jin Gao

We introduce a framework that enables both multi-view character consistency and 3D camera control in video diffusion models through a novel customization data pipeline. We train the character consistency component with recorded volumetric…

Computer Vision and Pattern Recognition · Computer Science 2025-10-17 Yuancheng Xu , Wenqi Xian , Li Ma , Julien Philip , Ahmet Levent Taşel , Yiwei Zhao , Ryan Burgert , Mingming He , Oliver Hermann , Oliver Pilarski , Rahul Garg , Paul Debevec , Ning Yu

We propose a novel-view augmentation (NOVA) strategy to train NeRFs for photo-realistic 3D composition of dynamic objects in a static scene. Compared to prior work, our framework significantly reduces blending artifacts when inserting…

Computer Vision and Pattern Recognition · Computer Science 2023-08-25 Dakshit Agrawal , Jiajie Xu , Siva Karthik Mustikovela , Ioannis Gkioulekas , Ashish Shrivastava , Yuning Chai

Video-conditioned 4D shape generation aims to recover time-varying 3D geometry and view-consistent appearance directly from an input video. In this work, we introduce a native video-to-4D shape generation framework that synthesizes a single…

Computer Vision and Pattern Recognition · Computer Science 2025-10-08 Jiraphon Yenphraphai , Ashkan Mirzaei , Jianqi Chen , Jiaxu Zou , Sergey Tulyakov , Raymond A. Yeh , Peter Wonka , Chaoyang Wang

Simulating robot-world interactions is a cornerstone of Embodied AI. Recently, a few works have shown promise in leveraging video generations to transcend the rigid visual/physical constraints of traditional simulators. However, they…

Robotics · Computer Science 2026-03-18 Mutian Xu , Tianbao Zhang , Tianqi Liu , Zhaoxi Chen , Xiaoguang Han , Ziwei Liu

Given the high complexity of directly generating high-dimensional data such as 4D, we present 4DVD, a cascaded video diffusion model that generates 4D content in a decoupled manner. Unlike previous multi-view video methods that directly…

Computer Vision and Pattern Recognition · Computer Science 2025-08-07 Shuzhou Yang , Xiaodong Cun , Xiaoyu Li , Yaowei Li , Jian Zhang

Recent 3D world modeling systems based on generative scene synthesis, such as Marble, can create coherent and explorable 3D environments, yet their outputs are typically static monolithic assets with limited editability and physical…

Computer Vision and Pattern Recognition · Computer Science 2026-05-18 Jichen Hu , Jiawei Guo , Jiazhong Cen , Chen Yang , Sikuang Li , Wei Shen

World-model-based imagine-then-act becomes a promising paradigm for robotic manipulation, yet existing approaches typically support either purely image-based forecasting or reasoning over partial 3D geometry, limiting their ability to…

Computer Vision and Pattern Recognition · Computer Science 2026-05-27 Jiaxu Wang , Yicheng Jiang , Tianlun He , Jingkai Sun , Qiang Zhang , Junhao He , Jiahang Cao , Zesen Gan , Mingyuan Sun , Qiming Shao , Xiangyu Yue