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Related papers: 3DreamBooth: High-Fidelity 3D Subject-Driven Video…

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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

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

In this work, we present MotionBooth, an innovative framework designed for animating customized subjects with precise control over both object and camera movements. By leveraging a few images of a specific object, we efficiently fine-tune a…

Computer Vision and Pattern Recognition · Computer Science 2024-10-30 Jianzong Wu , Xiangtai Li , Yanhong Zeng , Jiangning Zhang , Qianyu Zhou , Yining Li , Yunhai Tong , Kai Chen

Customized text-to-video generation aims to generate text-guided videos with user-given subjects, which has gained increasing attention. However, existing works are primarily limited to single-subject oriented text-to-video generation,…

Computer Vision and Pattern Recognition · Computer Science 2025-04-15 Hong Chen , Xin Wang , Guanning Zeng , Yipeng Zhang , Yuwei Zhou , Feilin Han , Yaofei Wu , Wenwu Zhu

We introduce AvatarBooth, a novel method for generating high-quality 3D avatars using text prompts or specific images. Unlike previous approaches that can only synthesize avatars based on simple text descriptions, our method enables the…

Computer Vision and Pattern Recognition · Computer Science 2023-06-19 Yifei Zeng , Yuanxun Lu , Xinya Ji , Yao Yao , Hao Zhu , Xun Cao

Recent years have witnessed the strong power of 3D generation models, which offer a new level of creative flexibility by allowing users to guide the 3D content generation process through a single image or natural language. However, it…

Computer Vision and Pattern Recognition · Computer Science 2024-03-15 Fangfu Liu , Hanyang Wang , Weiliang Chen , Haowen Sun , Yueqi Duan

Recent advances in customized video generation have enabled users to create videos tailored to both specific subjects and motion trajectories. However, existing methods often require complicated test-time fine-tuning and struggle with…

Computer Vision and Pattern Recognition · Computer Science 2024-10-18 Yujie Wei , Shiwei Zhang , Hangjie Yuan , Xiang Wang , Haonan Qiu , Rui Zhao , Yutong Feng , Feng Liu , Zhizhong Huang , Jiaxin Ye , Yingya Zhang , Hongming Shan

Large text-to-image models achieved a remarkable leap in the evolution of AI, enabling high-quality and diverse synthesis of images from a given text prompt. However, these models lack the ability to mimic the appearance of subjects in a…

Computer Vision and Pattern Recognition · Computer Science 2023-03-16 Nataniel Ruiz , Yuanzhen Li , Varun Jampani , Yael Pritch , Michael Rubinstein , Kfir Aberman

Subject-driven image generation aims at generating images containing customized subjects, which has recently drawn enormous attention from the research community. However, the previous works cannot precisely control the background and…

Computer Vision and Pattern Recognition · Computer Science 2023-08-21 Tianle Li , Max Ku , Cong Wei , Wenhu Chen

Customized video generation aims to produce videos that faithfully preserve the subject's appearance from reference images while maintaining temporally consistent motion from reference videos. Existing methods struggle to ensure both…

Computer Vision and Pattern Recognition · Computer Science 2025-12-16 Xuancheng Xu , Yaning Li , Sisi You , Bing-Kun Bao

Diffusion-based models have demonstrated impressive capabilities for text-to-image generation and are expected for personalized applications of subject-driven generation, which require the generation of customized concepts with one or a few…

Computer Vision and Pattern Recognition · Computer Science 2023-12-22 Miao Hua , Jiawei Liu , Fei Ding , Wei Liu , Jie Wu , Qian He

Text-driven video generation witnesses rapid progress. However, merely using text prompts is not enough to depict the desired subject appearance that accurately aligns with users' intents, especially for customized content creation. In this…

Computer Vision and Pattern Recognition · Computer Science 2023-12-04 Yuming Jiang , Tianxing Wu , Shuai Yang , Chenyang Si , Dahua Lin , Yu Qiao , Chen Change Loy , Ziwei Liu

Recent advancements in text-to-image diffusion models have shown remarkable creative capabilities with textual prompts, but generating personalized instances based on specific subjects, known as subject-driven generation, remains…

Computer Vision and Pattern Recognition · Computer Science 2024-10-11 Shanyan Guan , Yanhao Ge , Ying Tai , Jian Yang , Wei Li , Mingyu You

Recent advances in 3D generation have been remarkable, with methods such as DreamFusion leveraging large-scale text-to-image diffusion-based models to guide 3D object generation. These methods enable the synthesis of detailed and…

Computer Vision and Pattern Recognition · Computer Science 2024-10-23 Bohan Zeng , Shanglin Li , Yutang Feng , Ling Yang , Hong Li , Sicheng Gao , Jiaming Liu , Conghui He , Wentao Zhang , Jianzhuang Liu , Baochang Zhang , Shuicheng Yan

Customized video generation aims to generate high-quality videos guided by text prompts and subject's reference images. However, since it is only trained on static images, the fine-tuning process of subject learning disrupts abilities of…

Computer Vision and Pattern Recognition · Computer Science 2024-12-30 Tao Wu , Yong Zhang , Xintao Wang , Xianpan Zhou , Guangcong Zheng , Zhongang Qi , Ying Shan , Xi Li

Recent video diffusion models have made remarkable strides in visual quality, yet precise, fine-grained control remains a key bottleneck that limits practical customizability for content creation. For AI video creators, three forms of…

Computer Vision and Pattern Recognition · Computer Science 2026-03-17 Zhenghong Zhou , Xiaohang Zhan , Zhiqin Chen , Soo Ye Kim , Nanxuan Zhao , Haitian Zheng , Qing Liu , He Zhang , Zhe Lin , Yuqian Zhou , Jiebo Luo

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 advances in text-to-3D scene generation have demonstrated significant potential to transform content creation across multiple industries. Although the research community has made impressive progress in addressing the challenges of…

Text-to-3D generation, which synthesizes 3D assets according to an overall text description, has significantly progressed. However, a challenge arises when the specific appearances need customizing at designated viewpoints but referring…

Computer Vision and Pattern Recognition · Computer Science 2024-07-16 Junkai Yan , Yipeng Gao , Qize Yang , Xihan Wei , Xuansong Xie , Ancong Wu , Wei-Shi Zheng

While large-scale diffusion models have revolutionized video synthesis, achieving precise control over both multi-subject identity and multi-granularity motion remains a significant challenge. Recent attempts to bridge this gap often suffer…

Computer Vision and Pattern Recognition · Computer Science 2026-03-13 Yujie Wei , Xinyu Liu , Shiwei Zhang , Hangjie Yuan , Jinbo Xing , Zhekai Chen , Xiang Wang , Haonan Qiu , Rui Zhao , Yutong Feng , Ruihang Chu , Yingya Zhang , Yike Guo , Xihui Liu , Hongming Shan
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