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Generative models have achieved significant progress in advancing 2D image editing, demonstrating exceptional precision and realism. However, they often struggle with consistency and object identity preservation due to their inherent…

Computer Vision and Pattern Recognition · Computer Science 2025-07-09 Yuhuan Xie , Aoxuan Pan , Ming-Xian Lin , Wei Huang , Yi-Hua Huang , Xiaojuan Qi

Recent advances in diffusion models have significantly improved image editing. However, challenges persist in handling geometric transformations, such as translation, rotation, and scaling, particularly in complex scenes. Existing…

Computer Vision and Pattern Recognition · Computer Science 2026-02-10 Shuo Zhang , Wenzhuo Wu , Huayu Zhang , Jiarong Cheng , Xianghao Zang , Chao Ban , Hao Sun , Zhongjiang He , Tianwei Cao , Kongming Liang , Zhanyu Ma

We introduce Drag4D, an interactive framework that integrates object motion control within text-driven 3D scene generation. This framework enables users to define 3D trajectories for the 3D objects generated from a single image, seamlessly…

Computer Vision and Pattern Recognition · Computer Science 2025-09-29 Minjun Kang , Inkyu Shin , Taeyeop Lee , In So Kweon , Kuk-Jin Yoon

Recent advances in text-to-image (T2I) diffusion models have significantly improved semantic image editing, yet most methods fall short in performing 3D-aware object manipulation. In this work, we present FFSE, a 3D-aware autoregressive…

Computer Vision and Pattern Recognition · Computer Science 2025-11-18 Xincheng Shuai , Zhenyuan Qin , Henghui Ding , Dacheng Tao

Controllable 3D scene generation has extensive applications in virtual reality and interior design, where the generated scenes should exhibit high levels of realism and controllability in terms of geometry. Scene graphs provide a suitable…

Computer Vision and Pattern Recognition · Computer Science 2025-03-27 Zhifei Yang , Keyang Lu , Chao Zhang , Jiaxing Qi , Hanqi Jiang , Ruifei Ma , Shenglin Yin , Yifan Xu , Mingzhe Xing , Zhen Xiao , Jieyi Long , Guangyao Zhai

Despite the ability of existing large-scale text-to-image (T2I) models to generate high-quality images from detailed textual descriptions, they often lack the ability to precisely edit the generated or real images. In this paper, we propose…

Computer Vision and Pattern Recognition · Computer Science 2023-11-21 Chong Mou , Xintao Wang , Jiechong Song , Ying Shan , Jian Zhang

We introduce InseRF, a novel method for generative object insertion in the NeRF reconstructions of 3D scenes. Based on a user-provided textual description and a 2D bounding box in a reference viewpoint, InseRF generates new objects in 3D…

Computer Vision and Pattern Recognition · Computer Science 2024-01-11 Mohamad Shahbazi , Liesbeth Claessens , Michael Niemeyer , Edo Collins , Alessio Tonioni , Luc Van Gool , Federico Tombari

Advances in image diffusion models have recently led to notable improvements in the generation of high-quality images. In combination with Neural Radiance Fields (NeRFs), they enabled new opportunities in 3D generation. However, most…

Computer Vision and Pattern Recognition · Computer Science 2024-03-28 Jan-Niklas Dihlmann , Andreas Engelhardt , Hendrik Lensch

3D generation methods have shown visually compelling results powered by diffusion image priors. However, they often fail to produce realistic geometric details, resulting in overly smooth surfaces or geometric details inaccurately baked in…

Computer Vision and Pattern Recognition · Computer Science 2024-12-10 Ruihan Gao , Kangle Deng , Gengshan Yang , Wenzhen Yuan , Jun-Yan Zhu

Recently, the surge of efficient and automated 3D AI-generated content (AIGC) methods has increasingly illuminated the path of transforming human imagination into complex 3D structures. However, the automated generation of 3D content is…

Graphics · Computer Science 2024-12-20 Pei Chen , Fudong Wang , Yixuan Tong , Jingdong Chen , Ming Yang , Minghui Yang

The advancement of diffusion models has pushed the boundary of text-to-3D object generation. While it is straightforward to composite objects into a scene with reasonable geometry, it is nontrivial to texture such a scene perfectly due to…

Computer Vision and Pattern Recognition · Computer Science 2024-06-05 Qi Wang , Ruijie Lu , Xudong Xu , Jingbo Wang , Michael Yu Wang , Bo Dai , Gang Zeng , Dan Xu

User-friendly 3D object editing is a challenging task that has attracted significant attention recently. The limitations of direct 3D object editing without 2D prior knowledge have prompted increased attention towards utilizing 2D…

Graphics · Computer Science 2024-05-10 Sitian Shen , Jing Xu , Yuheng Yuan , Xingyi Yang , Qiuhong Shen , Xinchao Wang

This paper proposes a method for generating images of customized objects specified by users. The method is based on a general framework that bypasses the lengthy optimization required by previous approaches, which often employ a per-object…

Computer Vision and Pattern Recognition · Computer Science 2023-04-06 Xuhui Jia , Yang Zhao , Kelvin C. K. Chan , Yandong Li , Han Zhang , Boqing Gong , Tingbo Hou , Huisheng Wang , Yu-Chuan Su

In this work, we introduce Unique3D, a novel image-to-3D framework for efficiently generating high-quality 3D meshes from single-view images, featuring state-of-the-art generation fidelity and strong generalizability. Previous methods based…

Computer Vision and Pattern Recognition · Computer Science 2024-10-29 Kailu Wu , Fangfu Liu , Zhihan Cai , Runjie Yan , Hanyang Wang , Yating Hu , Yueqi Duan , Kaisheng Ma

We propose MagicQuill V2, a novel system that introduces a \textbf{layered composition} paradigm to generative image editing, bridging the gap between the semantic power of diffusion models and the granular control of traditional graphics…

Computer Vision and Pattern Recognition · Computer Science 2025-12-03 Zichen Liu , Yue Yu , Hao Ouyang , Qiuyu Wang , Shuailei Ma , Ka Leong Cheng , Wen Wang , Qingyan Bai , Yuxuan Zhang , Yanhong Zeng , Yixuan Li , Xing Zhu , Yujun Shen , Qifeng Chen

Utilizing pre-trained 2D large-scale generative models, recent works are capable of generating high-quality novel views from a single in-the-wild image. However, due to the lack of information from multiple views, these works encounter…

Computer Vision and Pattern Recognition · Computer Science 2024-03-27 Yunhan Yang , Yukun Huang , Xiaoyang Wu , Yuan-Chen Guo , Song-Hai Zhang , Hengshuang Zhao , Tong He , Xihui Liu

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

As 3D generation techniques continue to flourish, the demand for generating personalized content is rapidly rising. Users increasingly seek to apply various editing methods to polish generated 3D content, aiming to enhance its color, style,…

Computer Vision and Pattern Recognition · Computer Science 2025-08-12 Weitao Wang , Haoran Xu , Jun Meng , Haoqian Wang

Personalized text-to-image models allow users to generate varied styles of images (specified with a sentence) for an object (specified with a set of reference images). While remarkable results have been achieved using diffusion-based…

Computer Vision and Pattern Recognition · Computer Science 2024-07-19 Fanyue Wei , Wei Zeng , Zhenyang Li , Dawei Yin , Lixin Duan , Wen Li

Direct mesh editing and deformation are key components in the geometric modeling and animation pipeline. Mesh editing methods are typically framed as optimization problems combining user-specified vertex constraints with a regularizer that…

Graphics · Computer Science 2024-08-05 Tianhao Xie , Eugene Belilovsky , Sudhir Mudur , Tiberiu Popa