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With the widespread usage of VR devices and contents, demands for 3D scene generation techniques become more popular. Existing 3D scene generation models, however, limit the target scene to specific domain, primarily due to their training…

Computer Vision and Pattern Recognition · Computer Science 2023-11-27 Jaeyoung Chung , Suyoung Lee , Hyeongjin Nam , Jaerin Lee , Kyoung Mu Lee

Generating and editing a 3D scene guided by natural language poses a challenge, primarily due to the complexity of specifying the positional relations and volumetric changes within the 3D space. Recent advancements in Large Language Models…

Computer Vision and Pattern Recognition · Computer Science 2023-05-26 Yiqi Lin , Hao Wu , Ruichen Wang , Haonan Lu , Xiaodong Lin , Hui Xiong , Lin Wang

Recent remarkable advances in large-scale text-to-image diffusion models have inspired a significant breakthrough in text-to-3D generation, pursuing 3D content creation solely from a given text prompt. However, existing text-to-3D…

Computer Vision and Pattern Recognition · Computer Science 2023-11-10 Yang Chen , Yingwei Pan , Yehao Li , Ting Yao , Tao Mei

Recently, the impressive generative capabilities of diffusion models have been demonstrated, producing images with remarkable fidelity. Particularly, existing methods for the 3D object generation tasks, which is one of the fastest-growing…

Computer Vision and Pattern Recognition · Computer Science 2025-12-01 Jaeseok Lee , Jaekoo Lee

Recent advancements in text-to-3D generation have significantly contributed to the automation and democratization of 3D content creation. Building upon these developments, we aim to address the limitations of current methods in blending…

Computer Vision and Pattern Recognition · Computer Science 2024-08-26 Yeongtak Oh , Jooyoung Choi , Yongsung Kim , Minjun Park , Chaehun Shin , Sungroh Yoon

Recent advances in 3D generation have been remarkable, with methods such as DreamFusion leveraging large-scale text-to-image diffusion-based models to guide 3D object generation. These methods enable the synthesis of detailed and…

Computer Vision and Pattern Recognition · Computer Science 2024-10-23 Bohan Zeng , Shanglin Li , Yutang Feng , Ling Yang , Hong Li , Sicheng Gao , Jiaming Liu , Conghui He , Wentao Zhang , Jianzhuang Liu , Baochang Zhang , Shuicheng Yan

The recent advancements in text-to-3D generation mark a significant milestone in generative models, unlocking new possibilities for creating imaginative 3D assets across various real-world scenarios. While recent advancements in text-to-3D…

Computer Vision and Pattern Recognition · Computer Science 2023-12-05 Yixun Liang , Xin Yang , Jiantao Lin , Haodong Li , Xiaogang Xu , Yingcong Chen

Recent Multi-Modal Large Language Models (MLLMs) have demonstrated strong capabilities in learning joint representations from text and images. However, their spatial reasoning remains limited. We introduce 3DFroMLLM, a novel framework that…

Computer Vision and Pattern Recognition · Computer Science 2025-08-13 Noor Ahmed , Cameron Braunstein , Steffen Eger , Eddy Ilg

Text-driven 3D indoor scene generation is useful for gaming, the film industry, and AR/VR applications. However, existing methods cannot faithfully capture the room layout, nor do they allow flexible editing of individual objects in the…

Computer Vision and Pattern Recognition · Computer Science 2025-09-25 Chuan Fang , Yuan Dong , Kunming Luo , Xiaotao Hu , Rakesh Shrestha , Ping Tan

3D generation guided by text-to-image diffusion models enables the creation of visually compelling assets. However previous methods explore generation based on image or text. The boundaries of creativity are limited by what can be expressed…

Computer Vision and Pattern Recognition · Computer Science 2024-06-25 Sandeep Mishra , Oindrila Saha , Alan C. Bovik

We introduce "ImageDream," an innovative image-prompt, multi-view diffusion model for 3D object generation. ImageDream stands out for its ability to produce 3D models of higher quality compared to existing state-of-the-art,…

Computer Vision and Pattern Recognition · Computer Science 2023-12-06 Peng Wang , Yichun Shi

We present Layout-Your-3D, a framework that allows controllable and compositional 3D generation from text prompts. Existing text-to-3D methods often struggle to generate assets with plausible object interactions or require tedious…

Computer Vision and Pattern Recognition · Computer Science 2025-04-08 Junwei Zhou , Xueting Li , Lu Qi , Ming-Hsuan Yang

Generating realistic 3D indoor scenes from user inputs remains a challenging problem in computer vision and graphics, requiring careful balance of geometric consistency, spatial relationships, and visual realism. While neural generation…

Computer Vision and Pattern Recognition · Computer Science 2025-06-30 Mengqi Zhou , Xipeng Wang , Yuxi Wang , Zhaoxiang Zhang

In this paper, we introduce Uni3D-LLM, a unified framework that leverages a Large Language Model (LLM) to integrate tasks of 3D perception, generation, and editing within point cloud scenes. This framework empowers users to effortlessly…

Computer Vision and Pattern Recognition · Computer Science 2024-02-07 Dingning Liu , Xiaoshui Huang , Yuenan Hou , Zhihui Wang , Zhenfei Yin , Yongshun Gong , Peng Gao , Wanli Ouyang

Transformer based methods have enabled users to create, modify, and comprehend text and image data. Recently proposed Large Reconstruction Models (LRMs) further extend this by providing the ability to generate high-quality 3D models with…

Computer Vision and Pattern Recognition · Computer Science 2025-01-09 Kunal Kathare , Ankit Dhiman , K Vikas Gowda , Siddharth Aravindan , Shubham Monga , Basavaraja Shanthappa Vandrotti , Lokesh R Boregowda

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

Recently, the text-to-3D task has developed rapidly due to the appearance of the SDS method. However, the SDS method always generates 3D objects with poor quality due to the over-smooth issue. This issue is attributed to two factors: 1) the…

Computer Vision and Pattern Recognition · Computer Science 2025-01-07 Yiming Zhong , Xiaolin Zhang , Yao Zhao , Yunchao Wei

Recently, multi-view diffusion-based 3D generation methods have gained significant attention. However, these methods often suffer from shape and texture misalignment across generated multi-view images, leading to low-quality 3D generation…

Computer Vision and Pattern Recognition · Computer Science 2025-11-18 Zhuojiang Cai , Yiheng Zhang , Meitong Guo , Mingdao Wang , Yuwang Wang

Recently, the field of text-guided 3D scene generation has garnered significant attention. High-quality generation that aligns with physical realism and high controllability is crucial for practical 3D scene applications. However, existing…

Computer Vision and Pattern Recognition · Computer Science 2026-03-11 Yang Zhou , Zongjin He , Qixuan Li , Chao Wang

Given the steep learning curve of professional 3D software and the time-consuming process of managing large 3D assets, language-guided 3D scene editing has significant potential in fields such as virtual reality, augmented reality, and…

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