One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusion

Computer Vision and Pattern Recognition(2024)

引用 110|浏览5633
摘要
Recent advancements in open-world 3D object generation have been remarkable, with image-to-3D methods offering superior fine-grained control over their text-to-3D counterparts. However, most existing models fall short in simultaneously providing rapid generation speeds and high fidelity to input images - two features essential for practical applications. In this paper, we present One-2-3-45++, an innovative method that transforms a single image into a detailed 3D textured mesh in approximately one minute. Our approach aims to fully harness the extensive knowledge embedded in 2D diffusion models and priors from valuable yet limited 3D data. This is achieved by initially finetuning a 2D diffusion model for consistent multi-view image generation, followed by elevating these images to 3D with the aid of multi-view conditioned 3D native diffusion models. Extensive experimental evaluations demonstrate that our method can produce high-quality, diverse 3D assets that closely mirror the original input image. Our project webpage: https://sudo-ai-3d.github.io/One2345plus_page.
更多
查看译文
关键词
Single Image,3D Diffusion,Fine-tuned,Input Image,Diffusion Model,3D Data,Multi-view Images,2D Diffusion,User Study,3D Reconstruction,Image Object,Point Cloud,3D Volume,3D Mesh,3D Shape,Limited Availability Of Data,3D Representation,Network Diffusion,3D Convolution,Camera Pose,Signed Distance Function,Single Input Image,3D Voxel,High-resolution Volume,Feature Volume,Patch Features,Concurrent Work,Fine-tuning Process,Single Shape,Denoising
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
0
您的评分 :

暂无评分

数据免责声明
页面数据均来自互联网公开来源、合作出版商和通过AI技术自动分析结果,我们不对页面数据的有效性、准确性、正确性、可靠性、完整性和及时性做出任何承诺和保证。若有疑问,可以通过电子邮件方式联系我们:report@aminer.cn