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While text-to-video diffusion models have made significant strides, many still face challenges in generating videos with temporal consistency. Within diffusion frameworks, guidance techniques have proven effective in enhancing output…

Computer Vision and Pattern Recognition · Computer Science 2025-03-25 Hyelin Nam , Jaemin Kim , Dohun Lee , Jong Chul Ye

We propose a method for generating fly-through videos of a scene, from a single image and a given camera trajectory. We build upon an image-to-video latent diffusion model. We condition its UNet denoiser on the camera trajectory, using four…

Computer Vision and Pattern Recognition · Computer Science 2025-02-03 Stefan Popov , Amit Raj , Michael Krainin , Yuanzhen Li , William T. Freeman , Michael Rubinstein

Recent advances in Video Foundation Models (VFMs) have revolutionized human-centric video synthesis, yet fine-grained and independent editing of subjects and scenes remains a critical challenge. Recent attempts to incorporate richer…

Computer Vision and Pattern Recognition · Computer Science 2026-04-02 Fengyuan Yang , Luying Huang , Jiazhi Guan , Quanwei Yang , Dongwei Pan , Jianglin Fu , Haocheng Feng , Wei He , Kaisiyuan Wang , Hang Zhou , Angela Yao

We present DriveGen3D, a novel framework for generating high-quality and highly controllable dynamic 3D driving scenes that addresses critical limitations in existing methodologies. Current approaches to driving scene synthesis either…

Humans excel at forecasting the future dynamics of a scene given just a single image. Video generation models that can mimic this ability are an essential component for intelligent systems. Recent approaches have improved temporal coherence…

Computer Vision and Pattern Recognition · Computer Science 2026-05-18 Melonie de Almeida , Daniela Ivanova , Tong Shi , John H. Williamson , Paul Henderson

The "one-shot" technique represents a distinct and sophisticated aesthetic in filmmaking. However, its practical realization is often hindered by prohibitive costs and complex real-world constraints. Although emerging video generation…

Computer Vision and Pattern Recognition · Computer Science 2025-12-29 Jiawei Liu , Junqiao Li , Jiangfan Deng , Gen Li , Siyu Zhou , Zetao Fang , Shanshan Lao , Zengde Deng , Jianing Zhu , Tingting Ma , Jiayi Li , Yunqiu Wang , Qian He , Xinglong Wu

In this paper, we investigate the generation of new video backgrounds given a human foreground video, a camera pose, and a reference scene image. This task presents three key challenges. First, the generated background should precisely…

We present a fast, spatio-temporal scene understanding framework based on Visual Geometry Grounded Transformer (VGGT). The proposed pipeline is designed to enable efficient, close to real-time performance, supporting applications including…

Computer Vision and Pattern Recognition · Computer Science 2025-12-01 Gergely Dinya , Péter Halász , András Lőrincz , Kristóf Karacs , Anna Gelencsér-Horváth

In this paper, we introduce \textbf{DimensionX}, a framework designed to generate photorealistic 3D and 4D scenes from just a single image with video diffusion. Our approach begins with the insight that both the spatial structure of a 3D…

Computer Vision and Pattern Recognition · Computer Science 2024-11-08 Wenqiang Sun , Shuo Chen , Fangfu Liu , Zilong Chen , Yueqi Duan , Jun Zhang , Yikai Wang

We propose FlashWorld, a generative model that produces 3D scenes from a single image or text prompt in seconds, 10~100$\times$ faster than previous works while possessing superior rendering quality. Our approach shifts from the…

Computer Vision and Pattern Recognition · Computer Science 2025-10-16 Xinyang Li , Tengfei Wang , Zixiao Gu , Shengchuan Zhang , Chunchao Guo , Liujuan Cao

Methods that synthesize indoor 3D scenes from text prompts have wide-ranging applications in film production, interior design, video games, virtual reality, and synthetic data generation for training embodied agents. Existing approaches…

Computer Vision and Pattern Recognition · Computer Science 2025-11-20 Antonio Ruiz , Tao Wu , Andrew Melnik , Qing Cheng , Xuqin Wang , Lu Liu , Yongliang Wang , Yanfeng Zhang , Helge Ritter

We present a method to map 2D image observations of a scene to a persistent 3D scene representation, enabling novel view synthesis and disentangled representation of the movable and immovable components of the scene. Motivated by the…

Computer Vision and Pattern Recognition · Computer Science 2023-04-11 Prafull Sharma , Ayush Tewari , Yilun Du , Sergey Zakharov , Rares Ambrus , Adrien Gaidon , William T. Freeman , Fredo Durand , Joshua B. Tenenbaum , Vincent Sitzmann

View-predictive generative models provide strong priors for lifting object-centric images and videos into 3D and 4D through rendering and score distillation objectives. A question then remains: what about lifting complete multi-object…

Computer Vision and Pattern Recognition · Computer Science 2024-05-24 Wen-Hsuan Chu , Lei Ke , Katerina Fragkiadaki

Cinematic storytelling is profoundly shaped by the artful manipulation of photographic elements such as depth of field and exposure. These effects are crucial in conveying mood and creating aesthetic appeal. However, controlling these…

Computer Vision and Pattern Recognition · Computer Science 2025-11-18 Huiqiang Sun , Liao Shen , Zhan Peng , Kun Wang , Size Wu , Yuhang Zang , Tianqi Liu , Zihao Huang , Xingyu Zeng , Zhiguo Cao , Wei Li , Chen Change Loy

Recent advances in Dense Simultaneous Localization and Mapping (SLAM) have demonstrated remarkable performance in static environments. However, dense SLAM in dynamic environments remains challenging. Most methods directly remove dynamic…

Robotics · Computer Science 2025-12-11 Siting Zhu , Yuxiang Huang , Wenhua Wu , Chaokang Jiang , Yongbo Chen , I-Ming Chen , Hesheng Wang

We introduce PAT3D, the first physics-augmented text-to-3D scene generation framework that integrates vision-language models with physics-based simulation to produce physically plausible, simulation-ready, and intersection-free 3D scenes.…

Computer Vision and Pattern Recognition · Computer Science 2026-04-24 Guying Lin , Kemeng Huang , Michael Liu , Ruihan Gao , Hanke Chen , Lyuhao Chen , Beijia Lu , Taku Komura , Yuan Liu , Jun-Yan Zhu , Minchen Li

In this work, we introduce CC3D, a conditional generative model that synthesizes complex 3D scenes conditioned on 2D semantic scene layouts, trained using single-view images. Different from most existing 3D GANs that limit their…

Computer Vision and Pattern Recognition · Computer Science 2023-09-12 Sherwin Bahmani , Jeong Joon Park , Despoina Paschalidou , Xingguang Yan , Gordon Wetzstein , Leonidas Guibas , Andrea Tagliasacchi

The ultimate goal of video generation is to satisfy a fundamental trilemma: achieving high visual quality, maintaining rigorous physical consistency, and enabling precise controllability. While recent models can maintain this balance in…

Computer Vision and Pattern Recognition · Computer Science 2026-03-12 Tianshuo Xu , Zhifei Chen , Leyi Wu , Hao Lu , Ying-cong Chen

Humans exhibit an innate capacity to rapidly perceive and segment objects from video observations, and even mentally assemble them into structured 3D scenes. Replicating such capability, termed compositional 3D reconstruction, is pivotal…

Computer Vision and Pattern Recognition · Computer Science 2026-04-14 Mingyu Dong , Chong Xia , Mingyuan Jia , Weichen Lyu , Long Xu , Zheng Zhu , Yueqi Duan

Robots operating in dynamic environments face significant challenges due to the presence of moving agents and displaced objects. Traditional SLAM systems typically assume a static world or treat dynamic as outliers, discarding their…

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