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Creating realistic 3D objects and clothed avatars from a single RGB image is an attractive yet challenging problem. Due to its ill-posed nature, recent works leverage powerful prior from 2D diffusion models pretrained on large datasets.…

Computer Vision and Pattern Recognition · Computer Science 2025-11-27 Yuxuan Xue , Xianghui Xie , Riccardo Marin , Gerard Pons-Moll

Existing text-based 3D generation methods generate attractive results but lack detailed geometry control. Sketches, known for their conciseness and expressiveness, have contributed to intuitive 3D modeling but are confined to producing…

Graphics · Computer Science 2024-05-15 Feng-Lin Liu , Hongbo Fu , Yu-Kun Lai , Lin Gao

The field of generative models has recently witnessed significant progress, with diffusion models showing remarkable performance in image generation. In light of this success, there is a growing interest in exploring the application of…

Computer Vision and Pattern Recognition · Computer Science 2023-06-21 Ariel Lapid , Idan Achituve , Lior Bracha , Ethan Fetaya

Recently, text-to-3D generation has attracted significant attention, resulting in notable performance enhancements. Previous methods utilize end-to-end 3D generation models to initialize 3D Gaussians, multi-view diffusion models to enforce…

Computer Vision and Pattern Recognition · Computer Science 2024-07-22 Shuo Huang , Shikun Sun , Zixuan Wang , Xiaoyu Qin , Yanmin Xiong , Yuan Zhang , Pengfei Wan , Di Zhang , Jia Jia

Sparse RGBD scene completion is a challenging task especially when considering consistent textures and geometries throughout the entire scene. Different from existing solutions that rely on human-designed text prompts or predefined camera…

Computer Vision and Pattern Recognition · Computer Science 2024-08-05 Ming-Feng Li , Yueh-Feng Ku , Hong-Xuan Yen , Chi Liu , Yu-Lun Liu , Albert Y. C. Chen , Cheng-Hao Kuo , Min Sun

Advances in 3D reconstruction have enabled high-quality 3D capture, but require a user to collect hundreds to thousands of images to create a 3D scene. We present CAT3D, a method for creating anything in 3D by simulating this real-world…

Computer Vision and Pattern Recognition · Computer Science 2024-05-17 Ruiqi Gao , Aleksander Holynski , Philipp Henzler , Arthur Brussee , Ricardo Martin-Brualla , Pratul Srinivasan , Jonathan T. Barron , Ben Poole

In recent years, the demand for 3D content has grown exponentially with the intelligent upgrade of interactive media, extended reality (XR), and Metaverse industries. In order to overcome the limitations of traditional manual modeling…

Graphics · Computer Science 2025-12-23 Xiang Tang , Ruotong Li , Xiaopeng Fan

High Dynamic Range (HDR) generation remains challenging for generative models, which are largely limited to low dynamic range outputs. Recent diffusionbased approaches approximate HDR by generating multiple exposure-conditioned samples,…

Computer Vision and Pattern Recognition · Computer Science 2026-05-13 Pedram Fekri , WenChen Li , William Chen , Peter Altamirano

Immersive scene generation, notably panorama creation, benefits significantly from the adaptation of large pre-trained text-to-image (T2I) models for multi-view image generation. Due to the high cost of acquiring multi-view images,…

Computer Vision and Pattern Recognition · Computer Science 2024-08-06 Aoming Liu , Zhong Li , Zhang Chen , Nannan Li , Yi Xu , Bryan A. Plummer

Acquiring detailed 3D scenes typically demands costly equipment, multi-view data, or labor-intensive modeling. Therefore, a lightweight alternative, generating complex 3D scenes from a single top-down image, plays an essential role in…

Computer Vision and Pattern Recognition · Computer Science 2025-10-07 Kaizhi Zheng , Ruijian Zha , Zishuo Xu , Jing Gu , Jie Yang , Xin Eric Wang

The ability to generate diverse 3D articulated head avatars is vital to a plethora of applications, including augmented reality, cinematography, and education. Recent work on text-guided 3D object generation has shown great promise in…

Computer Vision and Pattern Recognition · Computer Science 2023-07-12 Alexander W. Bergman , Wang Yifan , Gordon Wetzstein

With the rapid advancement and widespread adoption of VR/AR technologies, there is a growing demand for the creation of high-quality, immersive dynamic scenes. However, existing generation works predominantly concentrate on the creation of…

Computer Vision and Pattern Recognition · Computer Science 2025-08-22 Ke Xing , Hanwen Liang , Dejia Xu , Yuyang Yin , Konstantinos N. Plataniotis , Yao Zhao , Yunchao Wei

Text-to-3D scene generation holds immense potential for the gaming, film, and architecture sectors. Despite significant progress, existing methods struggle with maintaining high quality, consistency, and editing flexibility. In this paper,…

Computer Vision and Pattern Recognition · Computer Science 2024-07-22 Haoran Li , Haolin Shi , Wenli Zhang , Wenjun Wu , Yong Liao , Lin Wang , Lik-hang Lee , Pengyuan Zhou

Generating high-quality 360-degree views of human heads from single-view images is essential for enabling accessible immersive telepresence applications and scalable personalized content creation. While cutting-edge methods for full head…

Computer Vision and Pattern Recognition · Computer Science 2025-03-21 Yuming Gu , Phong Tran , Yujian Zheng , Hongyi Xu , Heyuan Li , Adilbek Karmanov , Hao Li

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

We present LT3SD, a novel latent diffusion model for large-scale 3D scene generation. Recent advances in diffusion models have shown impressive results in 3D object generation, but are limited in spatial extent and quality when extended to…

Computer Vision and Pattern Recognition · Computer Science 2025-05-02 Quan Meng , Lei Li , Matthias Nießner , Angela Dai

Despite increasingly realistic image quality, recent 3D image generative models often operate on 3D volumes of fixed extent with limited camera motions. We investigate the task of unconditionally synthesizing unbounded nature scenes,…

Computer Vision and Pattern Recognition · Computer Science 2023-03-24 Lucy Chai , Richard Tucker , Zhengqi Li , Phillip Isola , Noah Snavely

Digitizing the physical world into accurate simulation-ready virtual environments offers significant opportunities in a variety of fields such as augmented and virtual reality, gaming, and robotics. However, current 3D reconstruction and…

Computer Vision and Pattern Recognition · Computer Science 2025-10-08 Hongchi Xia , Chih-Hao Lin , Hao-Yu Hsu , Quentin Leboutet , Katelyn Gao , Michael Paulitsch , Benjamin Ummenhofer , Shenlong Wang

We present a method for relighting 3D reconstructions of large room-scale environments. Existing solutions for 3D scene relighting often require solving under-determined or ill-conditioned inverse rendering problems, and are as such unable…

Computer Vision and Pattern Recognition · Computer Science 2026-01-30 Xiaoyan Xing , Philipp Henzler , Junhwa Hur , Runze Li , Jonathan T. Barron , Pratul P. Srinivasan , Dor Verbin

There are two prevalent ways to constructing 3D scenes: procedural generation and 2D lifting. Among them, panorama-based 2D lifting has emerged as a promising technique, leveraging powerful 2D generative priors to produce immersive,…

Computer Vision and Pattern Recognition · Computer Science 2025-10-31 Yukun Huang , Jiwen Yu , Yanning Zhou , Jianan Wang , Xintao Wang , Pengfei Wan , Xihui Liu