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We introduce RealmDreamer, a technique for generating forward-facing 3D scenes from text descriptions. Our method optimizes a 3D Gaussian Splatting representation to match complex text prompts using pretrained diffusion models. Our key…

Computer Vision and Pattern Recognition · Computer Science 2025-03-12 Jaidev Shriram , Alex Trevithick , Lingjie Liu , Ravi Ramamoorthi

Recent progresses in large-scale text-to-image models have yielded remarkable accomplishments, finding various applications in art domain. However, expressing unique characteristics of an artwork (e.g. brushwork, colortone, or composition)…

Computer Vision and Pattern Recognition · Computer Science 2023-12-19 Namhyuk Ahn , Junsoo Lee , Chunggi Lee , Kunhee Kim , Daesik Kim , Seung-Hun Nam , Kibeom Hong

Textual image generation spans diverse fields like advertising, education, product packaging, social media, information visualization, and branding. Despite recent strides in language-guided image synthesis using diffusion models, current…

Computer Vision and Pattern Recognition · Computer Science 2024-05-22 Shubham Paliwal , Arushi Jain , Monika Sharma , Vikram Jamwal , Lovekesh Vig

Generative models for 3D object synthesis have seen significant advancements with the incorporation of prior knowledge distilled from 2D diffusion models. Nevertheless, challenges persist in the form of multi-view geometric inconsistencies…

Computer Vision and Pattern Recognition · Computer Science 2023-11-20 Lincong Feng , Muyu Wang , Maoyu Wang , Kuo Xu , Xiaoli Liu

Large-scale text-to-image diffusion models have achieved great success in synthesizing high-quality and diverse images given target text prompts. Despite the revolutionary image generation ability, current state-of-the-art models still…

Computer Vision and Pattern Recognition · Computer Science 2025-01-20 Jingyuan Zhu , Huimin Ma , Jiansheng Chen , Jian Yuan

Text-to-3D generation approaches have advanced significantly by leveraging pretrained 2D diffusion priors, producing high-quality and 3D-consistent outputs. However, they often fail to produce out-of-domain (OOD) or rare concepts, yielding…

Computer Vision and Pattern Recognition · Computer Science 2025-08-25 Yosef Dayani , Omer Benishu , Sagie Benaim

Text-to-image generation has made significant advancements with the introduction of text-to-image diffusion models. These models typically consist of a language model that interprets user prompts and a vision model that generates…

Computer Vision and Pattern Recognition · Computer Science 2024-03-13 Shihao Zhao , Shaozhe Hao , Bojia Zi , Huaizhe Xu , Kwan-Yee K. Wong

In this paper, we present a novel diffusion model called that generates multiview-consistent images from a single-view image. Using pretrained large-scale 2D diffusion models, recent work Zero123 demonstrates the ability to generate…

Computer Vision and Pattern Recognition · Computer Science 2024-04-16 Yuan Liu , Cheng Lin , Zijiao Zeng , Xiaoxiao Long , Lingjie Liu , Taku Komura , Wenping Wang

Novel view synthesis has observed tremendous developments since the arrival of NeRFs. However, Nerf models overfit on a single scene, lacking generalization to out of distribution objects. Recently, diffusion models have exhibited…

Computer Vision and Pattern Recognition · Computer Science 2024-03-14 Rukhshanda Hussain , Hui Xian Grace Lim , Borchun Chen , Mubarak Shah , Ser Nam Lim

Text-conditioned diffusion models can generate impressive images, but fall short when it comes to fine-grained control. Unlike direct-editing tools like Photoshop, text conditioned models require the artist to perform "prompt engineering,"…

Computer Vision and Pattern Recognition · Computer Science 2024-04-05 Michelle Shu , Charles Herrmann , Richard Strong Bowen , Forrester Cole , Ramin Zabih

The field of text-to-image (T2I) generation has made significant progress in recent years, largely driven by advancements in diffusion models. Linguistic control enables effective content creation, but struggles with fine-grained control…

Computer Vision and Pattern Recognition · Computer Science 2024-07-19 Yanan Sun , Yanchen Liu , Yinhao Tang , Wenjie Pei , Kai Chen

In this paper, we present a new text-guided 3D shape generation approach DreamStone that uses images as a stepping stone to bridge the gap between text and shape modalities for generating 3D shapes without requiring paired text and 3D data.…

Computer Vision and Pattern Recognition · Computer Science 2023-09-27 Zhengzhe Liu , Peng Dai , Ruihui Li , Xiaojuan Qi , Chi-Wing Fu

Diversity in image generation is essential to ensure fair representations and support creativity in ideation. Hence, many text-to-image models have implemented diversification mechanisms. Yet, after a few iterations of generation, a lack of…

Human-Computer Interaction · Computer Science 2025-06-25 M. Michelessa , J. Ng , C. Hurter , B. Y. Lim

We present Envision3D, a novel method for efficiently generating high-quality 3D content from a single image. Recent methods that extract 3D content from multi-view images generated by diffusion models show great potential. However, it is…

Computer Vision and Pattern Recognition · Computer Science 2024-03-15 Yatian Pang , Tanghui Jia , Yujun Shi , Zhenyu Tang , Junwu Zhang , Xinhua Cheng , Xing Zhou , Francis E. H. Tay , Li Yuan

The recent success of pre-trained diffusion models unlocks the possibility of the automatic generation of textures for arbitrary 3D meshes in the wild. However, these models are trained in the screen space, while converting them to a…

Computer Vision and Pattern Recognition · Computer Science 2024-06-28 Hongkun Zhang , Zherong Pan , Congyi Zhang , Lifeng Zhu , Xifeng Gao

Recent years have witnessed remarkable progress in multi-view diffusion models for 3D content creation. However, there remains a significant gap in image quality and prompt-following ability compared to 2D diffusion models. A critical…

Computer Vision and Pattern Recognition · Computer Science 2024-10-04 Zeyi Sun , Tong Wu , Pan Zhang , Yuhang Zang , Xiaoyi Dong , Yuanjun Xiong , Dahua Lin , Jiaqi Wang

We combine neural rendering with multi-modal image and text representations to synthesize diverse 3D objects solely from natural language descriptions. Our method, Dream Fields, can generate the geometry and color of a wide range of objects…

Computer Vision and Pattern Recognition · Computer Science 2022-05-05 Ajay Jain , Ben Mildenhall , Jonathan T. Barron , Pieter Abbeel , Ben Poole

Text-to-image generation has made remarkable progress with the emergence of diffusion models. However, it is still a difficult task to generate images for street views based on text, mainly because the road topology of street scenes is…

Computer Vision and Pattern Recognition · Computer Science 2024-02-08 Jinming Su , Songen Gu , Yiting Duan , Xingyue Chen , Junfeng Luo

3D texture swapping allows for the customization of 3D object textures, enabling efficient and versatile visual transformations in 3D editing. While no dedicated method exists, adapted 2D editing and text-driven 3D editing approaches can…

Computer Vision and Pattern Recognition · Computer Science 2025-10-17 Xiao Cao , Beibei Lin , Bo Wang , Zhiyong Huang , Robby T. Tan

Visual content creation has spurred a soaring interest given its applications in mobile photography and AR / VR. Style transfer and single-image 3D photography as two representative tasks have so far evolved independently. In this paper, we…

Computer Vision and Pattern Recognition · Computer Science 2021-12-07 Fangzhou Mu , Jian Wang , Yicheng Wu , Yin Li