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Generating realistic 3D objects from single-view images requires natural appearance, 3D consistency, and the ability to capture multiple plausible interpretations of unseen regions. Existing approaches often rely on fine-tuning pretrained…

Computer Vision and Pattern Recognition · Computer Science 2025-10-21 Pufan Li , Bi'an Du , Wei Hu

Recent works have explored text-guided image editing using diffusion models and generated edited images based on text prompts. However, the models struggle to accurately locate the regions to be edited and faithfully perform precise edits.…

Computer Vision and Pattern Recognition · Computer Science 2023-05-30 Qian Wang , Biao Zhang , Michael Birsak , Peter Wonka

Given a 3D mesh, we aim to synthesize 3D textures that correspond to arbitrary textual descriptions. Current methods for generating and assembling textures from sampled views often result in prominent seams or excessive smoothing. To tackle…

Computer Vision and Pattern Recognition · Computer Science 2024-08-05 Dong Huo , Zixin Guo , Xinxin Zuo , Zhihao Shi , Juwei Lu , Peng Dai , Songcen Xu , Li Cheng , Yee-Hong Yang

We propose a diffusion-based approach for Text-to-Image (T2I) generation with consistent and interactive 3D layout control and editing. While prior methods improve spatial adherence using 2D cues or iterative copy-warp-paste strategies,…

Computer Vision and Pattern Recognition · Computer Science 2026-01-21 Andrea Rigo , Luca Stornaiuolo , Weijie Wang , Mauro Martino , Bruno Lepri , Nicu Sebe

Perpetual view generation aims to synthesize a long-term video corresponding to an arbitrary camera trajectory solely from a single input image. Recent methods commonly utilize a pre-trained text-to-image diffusion model to synthesize new…

Computer Vision and Pattern Recognition · Computer Science 2025-06-24 Bo Pan , Yang Chen , Yingwei Pan , Ting Yao , Wei Chen , Tao Mei

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

Continuous image editing aims to provide slider-style control of edit strength while preserving source-image fidelity and maintaining a consistent edit direction. Existing learning-based slider methods typically rely on auxiliary modules…

Computer Vision and Pattern Recognition · Computer Science 2026-04-03 Taichi Endo , Guoqing Hao , Kazuhiko Sumi

World building with 3D scene representations is increasingly important for content creation, simulation, and interactive experiences, yet real workflows are inherently iterative: creators must repeatedly extend an existing scene under user…

Computer Vision and Pattern Recognition · Computer Science 2026-04-01 Zijian He , Renjie Liu , Yihao Wang , Weizhi Zhong , Huan Yuan , Kun Gai , Guangrun Wang , Guanbin Li

Recent works on text-to-3d generation show that using only 2D diffusion supervision for 3D generation tends to produce results with inconsistent appearances (e.g., faces on the back view) and inaccurate shapes (e.g., animals with extra…

Computer Vision and Pattern Recognition · Computer Science 2024-03-15 Cheng Chen , Xiaofeng Yang , Fan Yang , Chengzeng Feng , Zhoujie Fu , Chuan-Sheng Foo , Guosheng Lin , Fayao Liu

Recently large-scale language-image models (e.g., text-guided diffusion models) have considerably improved the image generation capabilities to generate photorealistic images in various domains. Based on this success, current image editing…

Computer Vision and Pattern Recognition · Computer Science 2023-05-09 Wenkai Dong , Song Xue , Xiaoyue Duan , Shumin Han

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

Recent work on image content manipulation based on vision-language pre-training models has been effectively extended to text-driven 3D scene editing. However, existing schemes for 3D scene editing still exhibit certain shortcomings,…

Computer Vision and Pattern Recognition · Computer Science 2024-07-11 Shuangkang Fang , Yufeng Wang , Yi-Hsuan Tsai , Yi Yang , Wenrui Ding , Shuchang Zhou , Ming-Hsuan Yang

Despite the typical inversion-then-editing paradigm using text-to-image (T2I) models has demonstrated promising results, directly extending it to text-to-video (T2V) models still suffers severe artifacts such as color flickering and content…

Computer Vision and Pattern Recognition · Computer Science 2025-03-20 Yukun Wang , Longguang Wang , Zhiyuan Ma , Qibin Hu , Kai Xu , Yulan Guo

Perpetual 3D scene generation aims to produce long-range and coherent 3D view sequences, which is applicable for long-term video synthesis and 3D scene reconstruction. Existing methods follow a "navigate-and-imagine" fashion and rely on…

Computer Vision and Pattern Recognition · Computer Science 2025-07-28 Chong Xia , Shengjun Zhang , Fangfu Liu , Chang Liu , Khodchaphun Hirunyaratsameewong , Yueqi Duan

With the emergence of large-scale Text-to-Image(T2I) models and implicit 3D representations like Neural Radiance Fields (NeRF), many text-driven generative editing methods based on NeRF have appeared. However, the implicit encoding of…

Computer Vision and Pattern Recognition · Computer Science 2024-11-18 Yanhao Sun , RunZe Tian , Xiao Han , XinYao Liu , Yan Zhang , Kai Xu

The slow inference process of image diffusion models significantly degrades interactive user experiences. To address this, we introduce Diffusion Preview, a novel paradigm employing rapid, low-step sampling to generate preliminary outputs…

Machine Learning · Computer Science 2026-04-08 Fu-Yun Wang , Hao Zhou , Liangzhe Yuan , Sanghyun Woo , Boqing Gong , Bohyung Han , Ming-Hsuan Yang , Han Zhang , Yukun Zhu , Ting Liu , Long Zhao

Recent progress in 4D representations, such as Dynamic NeRF and 4D Gaussian Splatting (4DGS), has enabled dynamic 4D scene reconstruction. However, text-driven 4D scene editing remains under-explored due to the challenge of ensuring both…

Computer Vision and Pattern Recognition · Computer Science 2025-12-02 Dong In Lee , Hyungjun Doh , Seunggeun Chi , Runlin Duan , Sangpil Kim , Karthik Ramani

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

Text-driven video editing enables users to modify video content only using text queries. While existing methods can modify video content if explicit descriptions of editing targets with precise spatial locations and temporal boundaries are…

Computer Vision and Pattern Recognition · Computer Science 2025-11-19 Yiqing Shen , Chenjia Li , Mathias Unberath

We present GSEdit, a pipeline for text-guided 3D object editing based on Gaussian Splatting models. Our method enables the editing of the style and appearance of 3D objects without altering their main details, all in a matter of minutes on…

Computer Vision and Pattern Recognition · Computer Science 2024-05-22 Francesco Palandra , Andrea Sanchietti , Daniele Baieri , Emanuele Rodolà
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