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Generative text-to-image models have gained great popularity among the public for their powerful capability to generate high-quality images based on natural language prompts. However, developing effective prompts for desired images can be…

Artificial Intelligence · Computer Science 2023-11-02 Yingchaojie Feng , Xingbo Wang , Kam Kwai Wong , Sijia Wang , Yuhong Lu , Minfeng Zhu , Baicheng Wang , Wei Chen

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

Text-to-3D generation from a single-view image is a popular but challenging task in 3D vision. Although numerous methods have been proposed, existing works still suffer from the inconsistency issues, including 1) semantic inconsistency, 2)…

Computer Vision and Pattern Recognition · Computer Science 2023-09-08 Yichen Ouyang , Wenhao Chai , Jiayi Ye , Dapeng Tao , Yibing Zhan , Gaoang Wang

Text entry is an essential task in our day-to-day digital interactions. Numerous intelligent features have been developed to streamline this process, making text entry more effective, efficient, and fluid. These improvements include…

Computation and Language · Computer Science 2023-10-17 Junxiao Shen , John J. Dudley , Jingyao Zheng , Bill Byrne , Per Ola Kristensson

Neural fields have achieved impressive advancements in view synthesis and scene reconstruction. However, editing these neural fields remains challenging due to the implicit encoding of geometry and texture information. In this paper, we…

Computer Vision and Pattern Recognition · Computer Science 2023-09-08 Jingyu Zhuang , Chen Wang , Lingjie Liu , Liang Lin , Guanbin Li

In this work, we explore the challenging task of generating 3D shapes from text. Beyond the existing works, we propose a new approach for text-guided 3D shape generation, capable of producing high-fidelity shapes with colors that match the…

Computer Vision and Pattern Recognition · Computer Science 2022-03-29 Zhengzhe Liu , Yi Wang , Xiaojuan Qi , Chi-Wing Fu

Recent advances in generative modeling have driven significant progress in text-guided texture synthesis. However, current methods focus on synthesizing texture for single static 3D object, and struggle to handle entire families of shapes,…

Graphics · Computer Science 2025-10-07 Ruiqi Xu , Zihan Zhu , Ben Ahlbrand , Srinath Sridhar , Daniel Ritchie

While large-scale datasets have driven significant progress in Text-to-Video (T2V) generative models, these models remain highly sensitive to input prompts, demonstrating that prompt design is critical to generation quality. Current methods…

Computer Vision and Pattern Recognition · Computer Science 2026-03-03 Zillur Rahman , Alex Sheng , Cristian Meo

Prompt learning is an effective paradigm that bridges gaps between the pre-training tasks and the corresponding downstream applications. Approaches based on this paradigm have achieved great transcendent results in various applications.…

Information Retrieval · Computer Science 2022-09-26 Zhigang Kan , Linhui Feng , Zhangyue Yin , Linbo Qiao , Xipeng Qiu , Dongsheng Li

Existing generative approaches for guided image synthesis of multi-object scenes typically rely on 2D controls in the image or text space. As a result, these methods struggle to maintain and respect consistent three-dimensional geometric…

Computer Vision and Pattern Recognition · Computer Science 2025-08-05 Léopold Maillard , Tom Durand , Adrien Ramanana Rahary , Maks Ovsjanikov

Vision-language models (VLMs) have made significant progress in image classification by training with large-scale paired image-text data. Their performances largely depend on the prompt quality. While recent methods show that visual…

Computer Vision and Pattern Recognition · Computer Science 2026-02-12 Xiangyan Qu , Gaopeng Gou , Jiamin Zhuang , Jing Yu , Kun Song , Qihao Wang , Yili Li , Gang Xiong

Recent advancements in generative models have enabled the creation of dynamic 4D content - 3D objects in motion - based on text prompts, which holds potential for applications in virtual worlds, media, and gaming. Existing methods provide…

Computer Vision and Pattern Recognition · Computer Science 2025-05-28 Ohad Rahamim , Ori Malca , Dvir Samuel , Gal Chechik

Text-to-image generation has progressed rapidly, but faithfully generating complex scenes requires extensive trial-and-error to find the exact prompt. In the prompt inversion task, the goal is to recover a textual prompt that can faithfully…

Machine Learning · Computer Science 2026-04-30 Asaf Buchnick , Aviv Shamsian , Aviv Navon , Ethan Fetaya

Prompt-driven scene synthesis allows users to generate complete 3D environments from textual descriptions. Current text-to-scene methods often struggle with complex geometries and object transformations, and tend to show weak adherence to…

Computer Vision and Pattern Recognition · Computer Science 2025-11-14 Frédéric Berdoz , Luca A. Lanzendörfer , Nick Tuninga , Roger Wattenhofer

Facilitated by large language models (LLMs), personalized text generation has become a rapidly growing research direction. Most existing studies focus on designing specialized models for a particular domain, or they require fine-tuning the…

Computation and Language · Computer Science 2024-02-09 Cheng Li , Mingyang Zhang , Qiaozhu Mei , Weize Kong , Michael Bendersky

Recent multimodal large language models have achieved strong performance in unified text and image understanding and generation, yet extending such native capability to 3D remains challenging due to limited data. Compared to abundant 2D…

Computer Vision and Pattern Recognition · Computer Science 2026-04-03 Chongjie Ye , Cheng Cao , Chuanyu Pan , Yiming Hao , Yihao Zhi , Yuanming Hu , Xiaoguang Han

Text-to-3D form plays a crucial role in creating editable 3D scenes for AR/VR. Recent advances have shown promise in merging neural radiance fields (NeRFs) with pre-trained diffusion models for text-to-3D object generation. However, one…

Computer Vision and Pattern Recognition · Computer Science 2024-09-25 Haotian Bai , Yuanhuiyi Lyu , Lutao Jiang , Sijia Li , Haonan Lu , Xiaodong Lin , Lin Wang

The text-to-image synthesis by diffusion models has recently shown remarkable performance in generating high-quality images. Although performs well for simple texts, the models may get confused when faced with complex texts that contain…

Computer Vision and Pattern Recognition · Computer Science 2024-01-15 Chang Yu , Junran Peng , Xiangyu Zhu , Zhaoxiang Zhang , Qi Tian , Zhen Lei

3D editing plays a crucial role in editing and reusing existing 3D assets, thereby enhancing productivity. Recently, 3DGS-based methods have gained increasing attention due to their efficient rendering and flexibility. However, achieving…

Graphics · Computer Science 2024-12-12 Yian Zhao , Wanshi Xu , Yang Wu , Weiheng Huang , Zhongqian Sun , Wei Yang

Recent virtual try-on approaches have advanced by finetuning pre-trained text-to-image diffusion models to leverage their powerful generative ability. However, the use of text prompts in virtual try-on remains underexplored. This paper…

Computer Vision and Pattern Recognition · Computer Science 2025-08-08 Jeongho Kim , Hoiyeong Jin , Sunghyun Park , Jaegul Choo
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