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Recent advancements in image generation have made significant progress, yet existing models present limitations in perceiving and generating an arbitrary number of interrelated images within a broad context. This limitation becomes…

Computer Vision and Pattern Recognition · Computer Science 2024-04-05 Ying Shen , Yizhe Zhang , Shuangfei Zhai , Lifu Huang , Joshua M. Susskind , Jiatao Gu

Current diffusion-based text-to-video methods are limited to producing short video clips of a single shot and lack the capability to generate multi-shot videos with discrete transitions where the same character performs distinct activities…

Computer Vision and Pattern Recognition · Computer Science 2025-05-13 Ozgur Kara , Krishna Kumar Singh , Feng Liu , Duygu Ceylan , James M. Rehg , Tobias Hinz

Existing works in single-image human reconstruction suffer from weak generalizability due to insufficient training data or 3D inconsistencies for a lack of comprehensive multi-view knowledge. In this paper, we introduce MagicMan, a…

Computer Vision and Pattern Recognition · Computer Science 2024-08-27 Xu He , Xiaoyu Li , Di Kang , Jiangnan Ye , Chaopeng Zhang , Liyang Chen , Xiangjun Gao , Han Zhang , Zhiyong Wu , Haolin Zhuang

Diffusion models have become central to various image editing tasks, yet they often fail to fully adhere to physical laws, particularly with effects like shadows, reflections, and occlusions. In this work, we address the challenge of…

Computer Vision and Pattern Recognition · Computer Science 2025-04-23 Ankit Dhiman , Manan Shah , R Venkatesh Babu

Inspired by generative paradigms in image and video, 3D shape generation has made notable progress, enabling the rapid synthesis of high-fidelity 3D assets from a single image. However, current methods still face challenges, including the…

Computer Vision and Pattern Recognition · Computer Science 2025-11-26 Yangguang Li , Xianglong He , Zi-Xin Zou , Zexiang Liu , Wanli Ouyang , Ding Liang , Yan-Pei Cao

Existing single image-to-3D creation methods typically involve a two-stage process, first generating multi-view images, and then using these images for 3D reconstruction. However, training these two stages separately leads to significant…

Computer Vision and Pattern Recognition · Computer Science 2025-05-02 Hao Wen , Zehuan Huang , Yaohui Wang , Xinyuan Chen , Lu Sheng

It is inherently ambiguous to lift 2D results from pre-trained diffusion models to a 3D world for text-to-3D generation. 2D diffusion models solely learn view-agnostic priors and thus lack 3D knowledge during the lifting, leading to the…

Computer Vision and Pattern Recognition · Computer Science 2023-10-23 Weiyu Li , Rui Chen , Xuelin Chen , Ping Tan

Generating multi-view images based on text or single-image prompts is a critical capability for the creation of 3D content. Two fundamental questions on this topic are what data we use for training and how to ensure multi-view consistency.…

Computer Vision and Pattern Recognition · Computer Science 2024-03-19 Qi Zuo , Xiaodong Gu , Lingteng Qiu , Yuan Dong , Zhengyi Zhao , Weihao Yuan , Rui Peng , Siyu Zhu , Zilong Dong , Liefeng Bo , Qixing Huang

We present GSD, a diffusion model approach based on Gaussian Splatting (GS) representation for 3D object reconstruction from a single view. Prior works suffer from inconsistent 3D geometry or mediocre rendering quality due to improper…

Computer Vision and Pattern Recognition · Computer Science 2024-10-30 Yuxuan Mu , Xinxin Zuo , Chuan Guo , Yilin Wang , Juwei Lu , Xiaofeng Wu , Songcen Xu , Peng Dai , Youliang Yan , Li Cheng

This paper introduces MVDiffusion, a simple yet effective method for generating consistent multi-view images from text prompts given pixel-to-pixel correspondences (e.g., perspective crops from a panorama or multi-view images given depth…

Computer Vision and Pattern Recognition · Computer Science 2023-12-27 Shitao Tang , Fuyang Zhang , Jiacheng Chen , Peng Wang , Yasutaka Furukawa

We present MVD-Fusion: a method for single-view 3D inference via generative modeling of multi-view-consistent RGB-D images. While recent methods pursuing 3D inference advocate learning novel-view generative models, these generations are not…

Computer Vision and Pattern Recognition · Computer Science 2024-04-05 Hanzhe Hu , Zhizhuo Zhou , Varun Jampani , Shubham Tulsiani

As online shopping is growing, the ability for buyers to virtually visualize products in their settings-a phenomenon we define as "Virtual Try-All"-has become crucial. Recent diffusion models inherently contain a world model, rendering them…

Computer Vision and Pattern Recognition · Computer Science 2024-01-26 Mehmet Saygin Seyfioglu , Karim Bouyarmane , Suren Kumar , Amir Tavanaei , Ismail B. Tutar

We study the problem of synthesizing a long-term dynamic video from only a single image. This is challenging since it requires consistent visual content movements given large camera motions. Existing methods either hallucinate inconsistent…

Computer Vision and Pattern Recognition · Computer Science 2023-08-22 Liao Shen , Xingyi Li , Huiqiang Sun , Juewen Peng , Ke Xian , Zhiguo Cao , Guosheng Lin

Large-scale text-to-image models have demonstrated amazing ability to synthesize diverse and high-fidelity images. However, these models are often violated by several limitations. Firstly, they require the user to provide precise and…

Computer Vision and Pattern Recognition · Computer Science 2023-05-09 Yupei Lin , Sen Zhang , Xiaojun Yang , Xiao Wang , Yukai Shi

Recently, 3D generation methods have shown their powerful ability to automate 3D model creation. However, most 3D generation methods only rely on an input image or a text prompt to generate a 3D model, which lacks the control of each…

Computer Vision and Pattern Recognition · Computer Science 2025-07-08 Peng Li , Suizhi Ma , Jialiang Chen , Yuan Liu , Congyi Zhang , Wei Xue , Wenhan Luo , Alla Sheffer , Wenping Wang , Yike Guo

Recent remarkable improvements in large-scale text-to-image generative models have shown promising results in generating high-fidelity images. To further enhance editability and enable fine-grained generation, we introduce a…

Computer Vision and Pattern Recognition · Computer Science 2023-04-20 Kangyeol Kim , Sunghyun Park , Junsoo Lee , Jaegul Choo

Video generation has achieved remarkable progress with the introduction of diffusion models, which have significantly improved the quality of generated videos. However, recent research has primarily focused on scaling up model training,…

Computer Vision and Pattern Recognition · Computer Science 2025-01-16 Chenyang Si , Weichen Fan , Zhengyao Lv , Ziqi Huang , Yu Qiao , Ziwei Liu

We present a method for generating a full 360{\deg} orbit video around a person from a single input image. Existing methods typically adapt image-based diffusion models for multi-view synthesis, but yield inconsistent results across views…

Computer Vision and Pattern Recognition · Computer Science 2026-03-02 Keito Suzuki , Kunyao Chen , Lei Wang , Bang Du , Runfa Blark Li , Peng Liu , Ning Bi , Truong Nguyen

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

There has been tremendous progress in large-scale text-to-image synthesis driven by diffusion models enabling versatile downstream applications such as 3D object synthesis from texts, image editing, and customized generation. We present a…

Computer Vision and Pattern Recognition · Computer Science 2023-04-05 Ting-Hsuan Liao , Songwei Ge , Yiran Xu , Yao-Chih Lee , Badour AlBahar , Jia-Bin Huang
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