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Related papers: Hi3D: Pursuing High-Resolution Image-to-3D Generat…

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Humans can infer 3D structure from 2D images of an object based on past experience and improve their 3D understanding as they see more images. Inspired by this behavior, we introduce SAP3D, a system for 3D reconstruction and novel view…

Computer Vision and Pattern Recognition · Computer Science 2024-04-05 Xinyang Han , Zelin Gao , Angjoo Kanazawa , Shubham Goel , Yossi Gandelsman

Recent advances in generative AI have unveiled significant potential for the creation of 3D content. However, current methods either apply a pre-trained 2D diffusion model with the time-consuming score distillation sampling (SDS), or a…

Computer Vision and Pattern Recognition · Computer Science 2024-03-22 Yuanxun Lu , Jingyang Zhang , Shiwei Li , Tian Fang , David McKinnon , Yanghai Tsin , Long Quan , Xun Cao , Yao Yao

Existing feedforward image-to-3D methods mainly rely on 2D multi-view diffusion models that cannot guarantee 3D consistency. These methods easily collapse when changing the prompt view direction and mainly handle object-centric cases. In…

Computer Vision and Pattern Recognition · Computer Science 2025-10-14 Yuanhao Cai , He Zhang , Kai Zhang , Yixun Liang , Mengwei Ren , Fujun Luan , Qing Liu , Soo Ye Kim , Jianming Zhang , Zhifei Zhang , Yuqian Zhou , Yulun Zhang , Xiaokang Yang , Zhe Lin , Alan Yuille

Diffusion models have become a mainstream approach for high-resolution image synthesis. However, directly generating higher-resolution images from pretrained diffusion models will encounter unreasonable object duplication and exponentially…

Computer Vision and Pattern Recognition · Computer Science 2024-04-30 Shen Zhang , Zhaowei Chen , Zhenyu Zhao , Yuhao Chen , Yao Tang , Jiajun Liang

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

While recent years have witnessed great progress on using diffusion models for video generation, most of them are simple extensions of image generation frameworks, which fail to explicitly consider one of the key differences between videos…

Computer Vision and Pattern Recognition · Computer Science 2024-07-31 Jingyun Liang , Yuchen Fan , Kai Zhang , Radu Timofte , Luc Van Gool , Rakesh Ranjan

Despite recent advancements in neural 3D reconstruction, the dependence on dense multi-view captures restricts their broader applicability. In this work, we propose \textbf{ViewCrafter}, a novel method for synthesizing high-fidelity novel…

Computer Vision and Pattern Recognition · Computer Science 2024-09-04 Wangbo Yu , Jinbo Xing , Li Yuan , Wenbo Hu , Xiaoyu Li , Zhipeng Huang , Xiangjun Gao , Tien-Tsin Wong , Ying Shan , Yonghong Tian

Text-driven 3D editing enables user-friendly 3D object or scene editing with text instructions. Due to the lack of multi-view consistency priors, existing methods typically resort to employing 2D generation or editing models to process each…

Computer Vision and Pattern Recognition · Computer Science 2025-12-02 Liyi Chen , Ruihuang Li , Guowen Zhang , Pengfei Wang , Lei Zhang

We present Stable Video Diffusion - a latent video diffusion model for high-resolution, state-of-the-art text-to-video and image-to-video generation. Recently, latent diffusion models trained for 2D image synthesis have been turned into…

Computer Vision and Pattern Recognition · Computer Science 2023-11-28 Andreas Blattmann , Tim Dockhorn , Sumith Kulal , Daniel Mendelevitch , Maciej Kilian , Dominik Lorenz , Yam Levi , Zion English , Vikram Voleti , Adam Letts , Varun Jampani , Robin Rombach

In recent years, video generation has seen significant advancements. However, challenges still persist in generating complex motions and interactions. To address these challenges, we introduce ReVision, a plug-and-play framework that…

Computer Vision and Pattern Recognition · Computer Science 2026-01-12 Qihao Liu , Ju He , Qihang Yu , Liang-Chieh Chen , Alan Yuille

Latent Diffusion Models (LDMs) enable high-quality image synthesis while avoiding excessive compute demands by training a diffusion model in a compressed lower-dimensional latent space. Here, we apply the LDM paradigm to high-resolution…

Computer Vision and Pattern Recognition · Computer Science 2023-12-29 Andreas Blattmann , Robin Rombach , Huan Ling , Tim Dockhorn , Seung Wook Kim , Sanja Fidler , Karsten Kreis

Modern learning-based approaches to 3D-aware image synthesis achieve high photorealism and 3D-consistent viewpoint changes for the generated images. Existing approaches represent instances in a shared canonical space. However, for…

Computer Vision and Pattern Recognition · Computer Science 2024-04-15 Katja Schwarz , Seung Wook Kim , Jun Gao , Sanja Fidler , Andreas Geiger , Karsten Kreis

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

Single-image 3D scene reconstruction presents significant challenges due to its inherently ill-posed nature and limited input constraints. Recent advances have explored two promising directions: multiview generative models that train on 3D…

Computer Vision and Pattern Recognition · Computer Science 2025-04-17 Junlin Hao , Peiheng Wang , Haoyang Wang , Xinggong Zhang , Zongming Guo

The growing demand for Embodied AI and VR applications has highlighted the need for synthesizing high-quality 3D indoor scenes from sparse inputs. However, existing approaches struggle to infer massive amounts of missing geometry in large…

Computer Vision and Pattern Recognition · Computer Science 2026-04-15 Dehui Wang , Congsheng Xu , Rong Wei , Yue Shi , Shoufa Chen , Dingxiang Luo , Tianshuo Yang , Xiaokang Yang , Wei Sui , Yusen Qin , Rui Tang , Yao Mu

Generating 3D scenes is a challenging open problem, which requires synthesizing plausible content that is fully consistent in 3D space. While recent methods such as neural radiance fields excel at view synthesis and 3D reconstruction, they…

Computer Vision and Pattern Recognition · Computer Science 2024-02-22 Titas Anciukevičius , Fabian Manhardt , Federico Tombari , Paul Henderson

While image diffusion models have made significant progress in text-driven 3D content creation, they often fail to accurately capture the intended meaning of text prompts, especially for view information. This limitation leads to the Janus…

Computer Vision and Pattern Recognition · Computer Science 2024-03-22 Zhipeng Hu , Minda Zhao , Chaoyi Zhao , Xinyue Liang , Lincheng Li , Zeng Zhao , Changjie Fan , Xiaowei Zhou , Xin Yu

3D style transfer enables the creation of visually expressive 3D content, enriching the visual appearance of 3D scenes and objects. However, existing VGG- and CLIP-based methods struggle to model multi-view consistency within the model…

Computer Vision and Pattern Recognition · Computer Science 2026-01-28 Yitong Yang , Xuexin Liu , Yinglin Wang , Jing Wang , Hao Dou , Changshuo Wang , Shuting He

In this paper, we present VideoGen, a text-to-video generation approach, which can generate a high-definition video with high frame fidelity and strong temporal consistency using reference-guided latent diffusion. We leverage an…

Computer Vision and Pattern Recognition · Computer Science 2023-09-08 Xin Li , Wenqing Chu , Ye Wu , Weihang Yuan , Fanglong Liu , Qi Zhang , Fu Li , Haocheng Feng , Errui Ding , Jingdong Wang

In this paper, we study Text-to-3D content generation leveraging 2D diffusion priors to enhance the quality and detail of the generated 3D models. Recent progress (Magic3D) in text-to-3D has shown that employing high-resolution (e.g., 512 x…

Computer Vision and Pattern Recognition · Computer Science 2023-08-01 Jinbo Wu , Xiaobo Gao , Xing Liu , Zhengyang Shen , Chen Zhao , Haocheng Feng , Jingtuo Liu , Errui Ding
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