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We present DreamBooth3D, an approach to personalize text-to-3D generative models from as few as 3-6 casually captured images of a subject. Our approach combines recent advances in personalizing text-to-image models (DreamBooth) with…

Computer Vision and Pattern Recognition · Computer Science 2023-03-28 Amit Raj , Srinivas Kaza , Ben Poole , Michael Niemeyer , Nataniel Ruiz , Ben Mildenhall , Shiran Zada , Kfir Aberman , Michael Rubinstein , Jonathan Barron , Yuanzhen Li , Varun Jampani

Creating dynamic, view-consistent videos of customized subjects is highly sought after for a wide range of emerging applications, including immersive VR/AR, virtual production, and next-generation e-commerce. However, despite rapid progress…

Computer Vision and Pattern Recognition · Computer Science 2026-03-20 Hyun-kyu Ko , Jihyeon Park , Younghyun Kim , Dongheok Park , Eunbyung Park

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

Customized text-to-video generation aims to generate high-quality videos guided by text prompts and subject references. Current approaches for personalizing text-to-video generation suffer from tackling multiple subjects, which is a more…

Computer Vision and Pattern Recognition · Computer Science 2025-10-29 Zhao Wang , Aoxue Li , Lingting Zhu , Yong Guo , Qi Dou , Zhenguo Li

Recent research in subject-driven generation increasingly emphasizes the importance of selective subject features. Nevertheless, accurately selecting the content in a given reference image still poses challenges, especially when selecting…

Computer Vision and Pattern Recognition · Computer Science 2025-01-07 Junjie Hu , Shuyong Gao , Lingyi Hong , Qishan Wang , Yuzhou Zhao , Yan Wang , Wenqiang Zhang

In recent years, significant advancements have been made in text-driven 3D content generation. However, several challenges remain. In practical applications, users often provide extremely simple text inputs while expecting high-quality 3D…

Computer Vision and Pattern Recognition · Computer Science 2025-04-15 Huiqi Wu , Jianbo Mei , Yingjie Huang , Yining Xu , Jingjiao You , Yilong Liu , Li Yao

Creating a vivid video from the event or scenario in our imagination is a truly fascinating experience. Recent advancements in text-to-video synthesis have unveiled the potential to achieve this with prompts only. While text is convenient…

Computer Vision and Pattern Recognition · Computer Science 2023-06-02 Jinbo Xing , Menghan Xia , Yuxin Liu , Yuechen Zhang , Yong Zhang , Yingqing He , Hanyuan Liu , Haoxin Chen , Xiaodong Cun , Xintao Wang , Ying Shan , Tien-Tsin Wong

We introduce Follow-Your-Creation, a novel 4D video creation framework capable of both generating and editing 4D content from a single monocular video input. By leveraging a powerful video inpainting foundation model as a generative prior,…

Computer Vision and Pattern Recognition · Computer Science 2025-06-06 Yue Ma , Kunyu Feng , Xinhua Zhang , Hongyu Liu , David Junhao Zhang , Jinbo Xing , Yinhan Zhang , Ayden Yang , Zeyu Wang , Qifeng Chen

Customized generation using diffusion models has made impressive progress in image generation, but remains unsatisfactory in the challenging video generation task, as it requires the controllability of both subjects and motions. To that…

Computer Vision and Pattern Recognition · Computer Science 2023-12-08 Yujie Wei , Shiwei Zhang , Zhiwu Qing , Hangjie Yuan , Zhiheng Liu , Yu Liu , Yingya Zhang , Jingren Zhou , Hongming Shan

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

While 2D diffusion models have achieved remarkable success in identity-preserving personalization, extending this capability to 3D assets remains a significant challenge due to the complexities of multi-view consistency and spatial control.…

Computer Vision and Pattern Recognition · Computer Science 2026-05-19 Jinxin Ai , Matthias Nießner , Ziya Erkoç

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 3D generation are predominantly propelled by improvements in 3D-aware image diffusion models. These models are pretrained on Internet-scale image data and fine-tuned on massive 3D data, offering the capability of…

Computer Vision and Pattern Recognition · Computer Science 2024-10-03 Zeyu Yang , Zijie Pan , Chun Gu , Li Zhang

Recent progress in personalized image generation using diffusion models has been significant. However, development in the area of open-domain and non-fine-tuning personalized image generation is proceeding rather slowly. In this paper, we…

Computer Vision and Pattern Recognition · Computer Science 2024-05-21 Jian Ma , Junhao Liang , Chen Chen , Haonan Lu

In this work, we investigate the problem of creating high-fidelity 3D content from only a single image. This is inherently challenging: it essentially involves estimating the underlying 3D geometry while simultaneously hallucinating unseen…

Computer Vision and Pattern Recognition · Computer Science 2023-04-04 Junshu Tang , Tengfei Wang , Bo Zhang , Ting Zhang , Ran Yi , Lizhuang Ma , Dong Chen

Recent text-to-3D generation methods achieve impressive 3D content creation capacity thanks to the advances in image diffusion models and optimizing strategies. However, current methods struggle to generate correct 3D content for a complex…

Computer Vision and Pattern Recognition · Computer Science 2024-03-19 Xinhua Cheng , Tianyu Yang , Jianan Wang , Yu Li , Lei Zhang , Jian Zhang , Li Yuan

Generating customized content in videos has received increasing attention recently. However, existing works primarily focus on customized text-to-video generation for single subject, suffering from subject-missing and attribute-binding…

Computer Vision and Pattern Recognition · Computer Science 2024-05-22 Hong Chen , Xin Wang , Yipeng Zhang , Yuwei Zhou , Zeyang Zhang , Siao Tang , Wenwu Zhu

Automatic 3D content creation seeks to replace labor-intensive modeling and scanning pipelines with systems that can synthesize or recover 3D assets directly from text or images. Its applications span video games, virtual reality, robotics,…

Computer Vision and Pattern Recognition · Computer Science 2026-05-19 Jiahao Li

Text-guided diffusion models have shown superior performance in image/video generation and editing. While few explorations have been performed in 3D scenarios. In this paper, we discuss three fundamental and interesting problems on this…

Computer Vision and Pattern Recognition · Computer Science 2023-10-13 Gang Li , Heliang Zheng , Chaoyue Wang , Chang Li , Changwen Zheng , Dacheng Tao

The advancement of text-driven 3D content editing has been blessed by the progress from 2D generative diffusion models. However, a major obstacle hindering the widespread adoption of 3D content editing is its time-intensive processing. This…

Computer Vision and Pattern Recognition · Computer Science 2023-12-29 Liangchen Song , Liangliang Cao , Jiatao Gu , Yifan Jiang , Junsong Yuan , Hao Tang
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