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Related papers: CamCtrl3D: Single-Image Scene Exploration with Pre…

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With the popularity of monocular videos generated by video sharing and live broadcasting applications, reconstructing and editing dynamic scenes in stationary monocular cameras has become a special but anticipated technology. In contrast to…

Computer Vision and Pattern Recognition · Computer Science 2024-02-02 Weixing Xie , Xiao Dong , Yong Yang , Qiqin Lin , Jingze Chen , Junfeng Yao , Xiaohu Guo

Diffusion models generate images with an unprecedented level of quality, but how can we freely rearrange image layouts? Recent works generate controllable scenes via learning spatially disentangled latent codes, but these methods do not…

Computer Vision and Pattern Recognition · Computer Science 2024-04-11 Jiawei Ren , Mengmeng Xu , Jui-Chieh Wu , Ziwei Liu , Tao Xiang , Antoine Toisoul

The problem of generating a perpetual dynamic scene from a single view is an important problem with widespread applications in augmented and virtual reality, and robotics. However, since dynamic scenes regularly change over time, a key…

Computer Vision and Pattern Recognition · Computer Science 2025-08-05 Fengrui Tian , Tianjiao Ding , Jinqi Luo , Hancheng Min , René Vidal

Automatically generating a complete 3D scene from a text description, a reference image, or both has significant applications in fields like virtual reality and gaming. However, current methods often generate low-quality textures and…

Computer Vision and Pattern Recognition · Computer Science 2025-04-08 Zhexiao Xiong , Zhang Chen , Zhong Li , Yi Xu , Nathan Jacobs

Given a monocular video, the goal of video re-rendering is to generate views of the scene from a novel camera trajectory. Existing methods face two distinct challenges. Geometrically unconditioned models lack spatial awareness, leading to…

Computer Vision and Pattern Recognition · Computer Science 2026-04-03 Mingyang Xie , Numair Khan , Tianfu Wang , Naina Dhingra , Seonghyeon Nam , Haitao Yang , Zhuo Hui , Christopher Metzler , Andrea Vedaldi , Hamed Pirsiavash , Lei Luo

This study seeks to automate camera movement control for filming existing subjects into attractive videos, contrasting with the creation of non-existent content by directly generating the pixels. We select drone videos as our test case due…

Computer Vision and Pattern Recognition · Computer Science 2024-12-13 Yunzhong Hou , Liang Zheng , Philip Torr

Current methods for dense 3D point tracking in dynamic scenes typically rely on pairwise processing, require known camera poses, or assume temporal ordering of input frames, thereby constraining their flexibility and applicability.…

Computer Vision and Pattern Recognition · Computer Science 2026-04-06 Vivek Alumootil , Tuan-Anh Vu

Diffusion models excel in image generation but lack detailed semantic control using text prompts. Additional techniques have been developed to address this limitation. However, conditioning diffusion models solely on text-based descriptions…

Computer Vision and Pattern Recognition · Computer Science 2023-10-17 Frank Fundel

Existing diffusion-based 3D scene generation methods primarily operate in 2D image/video latent spaces, which makes maintaining cross-view appearance and geometric consistency inherently challenging. To bridge this gap, we present OneWorld,…

Computer Vision and Pattern Recognition · Computer Science 2026-03-18 Sensen Gao , Zhaoqing Wang , Qihang Cao , Dongdong Yu , Changhu Wang , Tongliang Liu , Mingming Gong , Jiawang Bian

The generation of 3D scenes from user-specified conditions offers a promising avenue for alleviating the production burden in 3D applications. Previous studies required significant effort to realize the desired scene, owing to limited…

Computer Vision and Pattern Recognition · Computer Science 2024-11-28 Takayuki Hara , Tatsuya Harada

Recent works have successfully extended large-scale text-to-image models to the video domain, producing promising results but at a high computational cost and requiring a large amount of video data. In this work, we introduce…

Computer Vision and Pattern Recognition · Computer Science 2024-05-24 Bo Peng , Xinyuan Chen , Yaohui Wang , Chaochao Lu , Yu Qiao

The increasing demand for virtual reality applications has highlighted the significance of crafting immersive 3D assets. We present a text-to-3D 360$^{\circ}$ scene generation pipeline that facilitates the creation of comprehensive…

Computer Vision and Pattern Recognition · Computer Science 2024-07-26 Shijie Zhou , Zhiwen Fan , Dejia Xu , Haoran Chang , Pradyumna Chari , Tejas Bharadwaj , Suya You , Zhangyang Wang , Achuta Kadambi

We study the problem of synthesizing a long-term dynamic video from only a single image. This is challenging since it requires consistent visual content movements given large camera motions. Existing methods either hallucinate inconsistent…

Computer Vision and Pattern Recognition · Computer Science 2023-08-22 Liao Shen , Xingyi Li , Huiqiang Sun , Juewen Peng , Ke Xian , Zhiguo Cao , Guosheng Lin

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

Camera control has been extensively studied in conditioned video generation; however, performing precisely altering the camera trajectories while faithfully preserving the video content remains a challenging task. The mainstream approach to…

Computer Vision and Pattern Recognition · Computer Science 2026-02-02 Dong-Yu Chen , Yixin Guo , Shuojin Yang , Tai-Jiang Mu , Shi-Min Hu

A recent frontier in computer vision has been the task of 3D video generation, which consists of generating a time-varying 3D representation of a scene. To generate dynamic 3D scenes, current methods explicitly model 3D temporal dynamics by…

Computer Vision and Pattern Recognition · Computer Science 2024-08-01 Rishab Parthasarathy , Zachary Ankner , Aaron Gokaslan

In this paper, we propose Extend3D, a training-free pipeline for 3D scene generation from a single image, built upon an object-centric 3D generative model. To overcome the limitations of fixed-size latent spaces in object-centric models for…

Computer Vision and Pattern Recognition · Computer Science 2026-04-01 Seungwoo Yoon , Jinmo Kim , Jaesik Park

We present Free4D, a novel tuning-free framework for 4D scene generation from a single image. Existing methods either focus on object-level generation, making scene-level generation infeasible, or rely on large-scale multi-view video…

Computer Vision and Pattern Recognition · Computer Science 2025-03-27 Tianqi Liu , Zihao Huang , Zhaoxi Chen , Guangcong Wang , Shoukang Hu , Liao Shen , Huiqiang Sun , Zhiguo Cao , Wei Li , Ziwei Liu

We present 3D Cinemagraphy, a new technique that marries 2D image animation with 3D photography. Given a single still image as input, our goal is to generate a video that contains both visual content animation and camera motion. We…

Computer Vision and Pattern Recognition · Computer Science 2023-03-13 Xingyi Li , Zhiguo Cao , Huiqiang Sun , Jianming Zhang , Ke Xian , Guosheng Lin

3D scene generation has long been dominated by 2D multi-view or video diffusion models. This is due not only to the lack of scene-level 3D latent representation, but also to the fact that most scene-level 3D visual data exists in the form…

Computer Vision and Pattern Recognition · Computer Science 2026-04-14 Dongxu Wei , Qi Xu , Zhiqi Li , Hangning Zhou , Cong Qiu , Hailong Qin , Mu Yang , Zhaopeng Cui , Peidong Liu