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3D assets are essential in the digital age. While automatic 3D generation, such as image-to-3d, has made significant strides in recent years, it often struggles to achieve fast, detailed, and high-fidelity generation simultaneously. In this…

Computer Vision and Pattern Recognition · Computer Science 2025-11-26 Huanning Dong , Yinuo Huang , Fan Li , Ping Kuang

Recent developments in 2D visual generation have been remarkably successful. However, 3D and 4D generation remain challenging in real-world applications due to the lack of large-scale 4D data and effective model design. In this paper, we…

Computer Vision and Pattern Recognition · Computer Science 2024-11-06 Yuyang Zhao , Chung-Ching Lin , Kevin Lin , Zhiwen Yan , Linjie Li , Zhengyuan Yang , Jianfeng Wang , Gim Hee Lee , Lijuan Wang

Inspired by generative paradigms in image and video, 3D shape generation has made notable progress, enabling the rapid synthesis of high-fidelity 3D assets from a single image. However, current methods still face challenges, including the…

Computer Vision and Pattern Recognition · Computer Science 2025-11-26 Yangguang Li , Xianglong He , Zi-Xin Zou , Zexiang Liu , Wanli Ouyang , Ding Liang , Yan-Pei Cao

3D content creation has long been a complex and time-consuming process, often requiring specialized skills and resources. While recent advancements have allowed for text-guided 3D object and scene generation, they still fall short of…

Computer Vision and Pattern Recognition · Computer Science 2024-08-06 Xingyi Li , Yizheng Wu , Jun Cen , Juewen Peng , Kewei Wang , Ke Xian , Zhe Wang , Zhiguo Cao , Guosheng Lin

We introduce 3inGAN, an unconditional 3D generative model trained from 2D images of a single self-similar 3D scene. Such a model can be used to produce 3D "remixes" of a given scene, by mapping spatial latent codes into a 3D volumetric…

Computer Vision and Pattern Recognition · Computer Science 2022-11-29 Animesh Karnewar , Oliver Wang , Tobias Ritschel , Niloy Mitra

Creating immersive and playable 3D worlds from texts or images remains a fundamental challenge in computer vision and graphics. Existing world generation approaches typically fall into two categories: video-based methods that offer rich…

Automating immersive VR scene creation remains a primary research challenge. Existing methods typically rely on complex geometry with post-simplification, resulting in inefficient pipelines or limited realism. In this paper, we introduce…

Graphics · Computer Science 2026-03-25 Jinyan Yuan , Bangbang Yang , Keke Wang , Panwang Pan , Lin Ma , Xuehai Zhang , Xiao Liu , Zhaopeng Cui , Yuewen Ma

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

We introduce MG-Gen, a framework that generates motion graphics directly from a single raster image. MG-Gen decompose a single raster image into layered structures represented as HTML, generate animation scripts for each layer, and then…

Graphics · Computer Science 2025-07-15 Takahiro Shirakawa , Tomoyuki Suzuki , Takuto Narumoto , Daichi Haraguchi

3D modeling is shifting from static visual representations toward physical, articulated assets that can be directly used in simulation and interaction. However, most existing 3D generation methods overlook key physical and articulation…

Computer Vision and Pattern Recognition · Computer Science 2025-11-18 Ziang Cao , Fangzhou Hong , Zhaoxi Chen , Liang Pan , Ziwei Liu

Our goal in this work is to generate realistic videos given just one initial frame as input. Existing unsupervised approaches to this task do not consider the fact that a video typically shows a 3D environment, and that this should remain…

Computer Vision and Pattern Recognition · Computer Science 2021-06-18 Paul Henderson , Christoph H. Lampert , Bernd Bickel

With the growing demand for high-fidelity 3D models from 2D images, existing methods still face significant challenges in accurately reproducing fine-grained geometric details due to limitations in domain gaps and inherent ambiguities in…

Graphics · Computer Science 2025-04-01 Chongjie Ye , Yushuang Wu , Ziteng Lu , Jiahao Chang , Xiaoyang Guo , Jiaqing Zhou , Hao Zhao , Xiaoguang Han

In this work, we introduce Unique3D, a novel image-to-3D framework for efficiently generating high-quality 3D meshes from single-view images, featuring state-of-the-art generation fidelity and strong generalizability. Previous methods based…

Computer Vision and Pattern Recognition · Computer Science 2024-10-29 Kailu Wu , Fangfu Liu , Zhihan Cai , Runjie Yan , Hanyang Wang , Yating Hu , Yueqi Duan , Kaisheng Ma

Generating high-fidelity, physically interactive 3D simulated tabletop scenes is essential for embodied AI -- especially for robotic manipulation policy learning and data synthesis. However, current text- or image-driven 3D scene generation…

Computer Vision and Pattern Recognition · Computer Science 2025-12-08 Ziqian Wang , Yonghao He , Licheng Yang , Wei Zou , Hongxuan Ma , Liu Liu , Wei Sui , Yuxin Guo , Hu Su

Previous works leveraging video models for image-to-3D scene generation tend to suffer from geometric distortions and blurry content. In this paper, we renovate the pipeline of image-to-3D scene generation by unlocking the potential of…

Computer Vision and Pattern Recognition · Computer Science 2025-12-01 Yuhao Wan , Lijuan Liu , Jingzhi Zhou , Zihan Zhou , Xuying Zhang , Dongbo Zhang , Shaohui Jiao , Qibin Hou , Ming-Ming Cheng

In the field of 3D content generation, single image scene reconstruction methods still struggle to simultaneously ensure the quality of individual assets and the coherence of the overall scene in complex environments, while texture editing…

Graphics · Computer Science 2026-02-18 Xiang Tang , Ruotong Li , Xiaopeng Fan

We present a computational framework that transforms single images into 3D physical objects. The visual geometry of a physical object in an image is determined by three orthogonal attributes: mechanical properties, external forces, and…

Computer Vision and Pattern Recognition · Computer Science 2025-01-03 Minghao Guo , Bohan Wang , Pingchuan Ma , Tianyuan Zhang , Crystal Elaine Owens , Chuang Gan , Joshua B. Tenenbaum , Kaiming He , Wojciech Matusik

Existing single-image 3D indoor scene generators often produce results that look visually plausible but fail to obey real-world physics, limiting their reliability in robotics, embodied AI, and design. To examine this gap, we introduce a…

Computer Vision and Pattern Recognition · Computer Science 2026-04-14 Dongli Wu , Jingyu Hu , Ka-Hei Hui , Xiaobao Wei , Chengwen Luo , Jianqiang Li , Zhengzhe Liu

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

Three-dimensional (3D) understanding of objects and scenes play a key role in humans' ability to interact with the world and has been an active area of research in computer vision, graphics, and robotics. Large scale synthetic and…

Computer Vision and Pattern Recognition · Computer Science 2024-12-11 Matthew Wallingford , Anand Bhattad , Aditya Kusupati , Vivek Ramanujan , Matt Deitke , Sham Kakade , Aniruddha Kembhavi , Roozbeh Mottaghi , Wei-Chiu Ma , Ali Farhadi