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3D scene generation conditioned on text prompts has significantly progressed due to the development of 2D diffusion generation models. However, the textual description of 3D scenes is inherently inaccurate and lacks fine-grained control…

Computer Vision and Pattern Recognition · Computer Science 2025-01-07 Minglin Chen , Longguang Wang , Sheng Ao , Ye Zhang , Kai Xu , Yulan Guo

Recent breakthroughs in text-to-4D generation rely on pre-trained text-to-image and text-to-video models to generate dynamic 3D scenes. However, current text-to-4D methods face a three-way tradeoff between the quality of scene appearance,…

Computer Vision and Pattern Recognition · Computer Science 2024-05-28 Sherwin Bahmani , Ivan Skorokhodov , Victor Rong , Gordon Wetzstein , Leonidas Guibas , Peter Wonka , Sergey Tulyakov , Jeong Joon Park , Andrea Tagliasacchi , David B. Lindell

Urban modeling is essential for city planning, scene synthesis, and gaming. Existing image-based methods generate diverse layouts but often lack geometric continuity and scalability, while graph-based methods capture structural relations…

Computer Vision and Pattern Recognition · Computer Science 2025-09-30 Mengyuan Niu , Xinxin Zhuo , Ruizhe Wang , Yuyue Huang , Junyan Yang , Qiao Wang

We present GuidedSceneGen, a text-to-3D generation framework that produces metrically accurate, globally consistent, and semantically interpretable indoor scenes. Unlike prior text-driven methods that often suffer from geometric drift or…

Computer Vision and Pattern Recognition · Computer Science 2026-03-17 Stefan Ainetter , Thomas Deixelberger , Edoardo A. Dominici , Philipp Drescher , Konstantinos Vardis , Markus Steinberger

Recent advancements in object-centric text-to-3D generation have shown impressive results. However, generating complex 3D scenes remains an open challenge due to the intricate relations between objects. Moreover, existing methods are…

Computer Vision and Pattern Recognition · Computer Science 2024-12-31 Yu-Hsiang Huang , Wei Wang , Sheng-Yu Huang , Yu-Chiang Frank Wang

Designing complex 3D scenes has been a tedious, manual process requiring domain expertise. Emerging text-to-3D generative models show great promise for making this task more intuitive, but existing approaches are limited to object-level…

Computer Vision and Pattern Recognition · Computer Science 2023-03-24 Ryan Po , Gordon Wetzstein

Generating high-quality 3D assets from textual descriptions remains a pivotal challenge in computer graphics and vision research. Due to the scarcity of 3D data, state-of-the-art approaches utilize pre-trained 2D diffusion priors, optimized…

Computer Vision and Pattern Recognition · Computer Science 2024-10-14 Ling Yang , Zixiang Zhang , Junlin Han , Bohan Zeng , Runjia Li , Philip Torr , Wentao Zhang

Text-guided image generation has progressed rapidly in recent years, inspiring major breakthroughs in text-guided shape generation. Recently, it has been shown that using score distillation, one can successfully text-guide a NeRF model to…

Computer Vision and Pattern Recognition · Computer Science 2022-11-15 Gal Metzer , Elad Richardson , Or Patashnik , Raja Giryes , Daniel Cohen-Or

Recent advances in text-driven 3D scene editing and stylization, which leverage the powerful capabilities of 2D generative models, have demonstrated promising outcomes. However, challenges remain in ensuring high-quality stylization and…

Graphics · Computer Science 2026-03-03 Haruo Fujiwara , Yusuke Mukuta , Tatsuya Harada

The ability to map descriptions of scenes to 3D geometric representations has many applications in areas such as art, education, and robotics. However, prior work on the text to 3D scene generation task has used manually specified object…

Computation and Language · Computer Science 2015-06-08 Angel Chang , Will Monroe , Manolis Savva , Christopher Potts , Christopher D. Manning

Text-to-3D generation has made remarkable progress recently, particularly with methods based on Score Distillation Sampling (SDS) that leverages pre-trained 2D diffusion models. While the usage of classifier-free guidance is well…

Computer Vision and Pattern Recognition · Computer Science 2023-11-01 Xin Yu , Yuan-Chen Guo , Yangguang Li , Ding Liang , Song-Hai Zhang , Xiaojuan Qi

3D indoor scene generation conditioned on short textual descriptions provides a promising avenue for interactive 3D environment construction without the need for labor-intensive layout specification. Despite recent progress in…

Computer Vision and Pattern Recognition · Computer Science 2026-04-09 Jie Feng , Jiawei Shen , Junjia Huang , Junpeng Zhang , Mingtao Feng , Weisheng Dong , Guanbin Li

Creating large-scale interactive 3D environments is essential for the development of Robotics and Embodied AI research. Current methods, including manual design, procedural generation, diffusion-based scene generation, and large language…

Computer Vision and Pattern Recognition · Computer Science 2024-11-18 Yian Wang , Xiaowen Qiu , Jiageng Liu , Zhehuan Chen , Jiting Cai , Yufei Wang , Tsun-Hsuan Wang , Zhou Xian , Chuang Gan

Text-driven 3D scene generation techniques have made rapid progress in recent years. Their success is mainly attributed to using existing generative models to iteratively perform image warping and inpainting to generate 3D scenes. However,…

Computer Vision and Pattern Recognition · Computer Science 2024-03-15 Frank Zhang , Yibo Zhang , Quan Zheng , Rui Ma , Wei Hua , Hujun Bao , Weiwei Xu , Changqing Zou

We present a method to generate 3D objects in styles. Our method takes a text prompt and a style reference image as input and reconstructs a neural radiance field to synthesize a 3D model with the content aligning with the text prompt and…

Computer Vision and Pattern Recognition · Computer Science 2025-02-14 Hubert Kompanowski , Binh-Son Hua

Text-to-3D is an emerging task that allows users to create 3D content with infinite possibilities. Existing works tackle the problem by optimizing a 3D representation with guidance from pre-trained diffusion models. An apparent drawback is…

Computer Vision and Pattern Recognition · Computer Science 2023-06-06 Yiji Cheng , Fei Yin , Xiaoke Huang , Xintong Yu , Jiaxiang Liu , Shikun Feng , Yujiu Yang , Yansong Tang

Recent text-to-scene generation approaches largely reduced the manual efforts required to create 3D scenes. However, their focus is either to generate a scene layout or to generate objects, and few generate both. The generated scene layout…

Computer Vision and Pattern Recognition · Computer Science 2026-04-30 Zhenggang Tang , Yuehao Wang , Yuchen Fan , Jun-Kun Chen , Yu-Ying Yeh , Kihyuk Sohn , Zhangyang Wang , Qixing Huang , Alexander Schwing , Rakesh Ranjan , Dilin Wang , Zhicheng Yan

Generating coherent and useful image/video scenes from a free-form textual description is technically a very difficult problem to handle. Textual description of the same scene can vary greatly from person to person, or sometimes even for…

Computer Vision and Pattern Recognition · Computer Science 2020-12-01 Faria Huq , Nafees Ahmed , Anindya Iqbal

As pretrained text-to-image diffusion models become increasingly powerful, recent efforts have been made to distill knowledge from these text-to-image pretrained models for optimizing a text-guided 3D model. Most of the existing methods…

Computer Vision and Pattern Recognition · Computer Science 2024-06-12 Gege Gao , Weiyang Liu , Anpei Chen , Andreas Geiger , Bernhard Schölkopf

In recent years, 3D generation has made great strides in both academia and industry. However, generating 3D scenes from a single RGB image remains a significant challenge, as current approaches often struggle to ensure both object…

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