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We introduce \textit{WonderVerse}, a simple but effective framework for generating extendable 3D scenes. Unlike existing methods that rely on iterative depth estimation and image inpainting, often leading to geometric distortions and…

Computer Vision and Pattern Recognition · Computer Science 2026-03-17 Hao Feng , Zhi Zuo , Jia-Hui Pan , Ka-Hei Hui , Qi Dou , Jingyu Hu , Zhengzhe Liu

3D scene generation has quickly become a challenging new research direction, fueled by consistent improvements of 2D generative diffusion models. Most prior work in this area generates scenes by iteratively stitching newly generated frames…

Computer Vision and Pattern Recognition · Computer Science 2024-05-01 Paul Engstler , Andrea Vedaldi , Iro Laina , Christian Rupprecht

We present WonderWorld, a novel framework for interactive 3D scene generation that enables users to interactively specify scene contents and layout and see the created scenes in low latency. The major challenge lies in achieving fast…

Computer Vision and Pattern Recognition · Computer Science 2025-03-26 Hong-Xing Yu , Haoyi Duan , Charles Herrmann , William T. Freeman , Jiajun Wu

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

Diffusion models have achieved impressive performance in video generation, but their iterative denoising process remains computationally expensive due to the large number of tokens processed at each timestep. Recently, progressive…

Computer Vision and Pattern Recognition · Computer Science 2026-05-21 Shikang Zheng , Jingkai Huang , Jiacheng Liu , Guantao Chen , Lixuan , Yuqi Lin , Peiliang Cai , Linfeng Zhang

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

Video outpainting aims to expand the visible content of a video beyond the original frame boundaries while preserving spatial fidelity and temporal coherence across frames. Existing methods primarily rely on large-scale generative models,…

Computer Vision and Pattern Recognition · Computer Science 2026-04-17 Inseok Jeon , Minhyeok Lee , Seunghoon Lee , Minseok Kang , Suhwan Cho , Sangyoun Lee

Motivated by discrete diffusion's success in language-vision modeling, we explore its potential for multi-view generation, a task dominated by continuous approaches. We introduce ViewMask-1-to-3, formulating multi-view synthesis as a…

Computer Vision and Pattern Recognition · Computer Science 2026-03-16 Ruishu Zhu , Zhihao Huang , Jiacheng Sun , Ping Luo , Hongyuan Zhang , Xuelong Li

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

Generating free-viewpoint videos is critical for immersive VR/AR experience but recent neural advances still lack the editing ability to manipulate the visual perception for large dynamic scenes. To fill this gap, in this paper we propose…

Computer Vision and Pattern Recognition · Computer Science 2021-05-03 Jiakai Zhang , Xinhang Liu , Xinyi Ye , Fuqiang Zhao , Yanshun Zhang , Minye Wu , Yingliang Zhang , Lan Xu , Jingyi Yu

Recent 3D novel view synthesis (NVS) methods often require extensive 3D data for training, and also typically lack generalization beyond the training distribution. Moreover, they tend to be object centric and struggle with complex and…

Computer Vision and Pattern Recognition · Computer Science 2025-11-18 Taewon Kang , Divya Kothandaraman , Dinesh Manocha , Ming C. Lin

This paper presents a method that allows users to design cinematic video shots in the context of image-to-video generation. Shot design, a critical aspect of filmmaking, involves meticulously planning both camera movements and object…

Computer Vision and Pattern Recognition · Computer Science 2025-02-07 Jinbo Xing , Long Mai , Cusuh Ham , Jiahui Huang , Aniruddha Mahapatra , Chi-Wing Fu , Tien-Tsin Wong , Feng Liu

Structure from motion (SfM) enables us to reconstruct a scene via casual capture from cameras at different viewpoints, and novel view synthesis (NVS) allows us to render a captured scene from a new viewpoint. Both are hard with casual…

Generative video modeling has emerged as a compelling tool to zero-shot reason about plausible physical interactions for open-world manipulation. Yet, it remains a challenge to translate such human-led motions into the low-level actions…

Robotics · Computer Science 2026-01-01 Karthik Dharmarajan , Wenlong Huang , Jiajun Wu , Li Fei-Fei , Ruohan Zhang

Recent text-to-image models have revolutionized image generation, but they still struggle with maintaining concept consistency across generated images. While existing works focus on character consistency, they often overlook the crucial…

Computer Vision and Pattern Recognition · Computer Science 2025-10-28 Quanjian Song , Donghao Zhou , Jingyu Lin , Fei Shen , Jiaze Wang , Xiaowei Hu , Cunjian Chen , Pheng-Ann Heng

Video generative models have made remarkable progress, yet they often yield visual artifacts that violate grounding in physical dynamics. Recent works such as PhysGen3D tackle single image-to-3D physics through mesh reconstruction and…

Computer Vision and Pattern Recognition · Computer Science 2026-05-19 Hwidong Kim , Yunho Kim , Tae-Kyun Kim

Novel view synthesis for dynamic $3$D scenes poses a significant challenge. Many notable efforts use NeRF-based approaches to address this task and yield impressive results. However, these methods rely heavily on sufficient motion parallax…

Computer Vision and Pattern Recognition · Computer Science 2025-07-01 Huiqiang Sun , Xingyi Li , Juewen Peng , Liao Shen , Zhiguo Cao , Ke Xian , Guosheng Lin

We introduce WonderJourney, a modularized framework for perpetual 3D scene generation. Unlike prior work on view generation that focuses on a single type of scenes, we start at any user-provided location (by a text description or an image)…

Computer Vision and Pattern Recognition · Computer Science 2024-04-15 Hong-Xing Yu , Haoyi Duan , Junhwa Hur , Kyle Sargent , Michael Rubinstein , William T. Freeman , Forrester Cole , Deqing Sun , Noah Snavely , Jiajun Wu , Charles Herrmann

Generating high-quality novel views of a scene from a single image requires maintaining structural coherence across different views, referred to as view consistency. While diffusion models have driven advancements in novel view synthesis,…

Computer Vision and Pattern Recognition · Computer Science 2025-08-07 Jiwoo Park , Tae Eun Choi , Youngjun Jun , Seong Jae Hwang

AI video generation has lowered barriers to video creation, but current tools still struggle with inconsistency. Filmmakers often find that clips fail to match characters and backgrounds, making it difficult to build coherent sequences. A…

Human-Computer Interaction · Computer Science 2025-12-22 Hye-Young Jo , Mose Sakashita , Aditi Mishra , Ryo Suzuki , Koichiro Niinuma , Aakar Gupta