One-2-3-45++：快速单图像到3D对象的生成，具有一致的多视角生成和3D扩散

摘要

最近在开放世界的3D物体生成方面取得了显著进展，图像到3D方法在细粒度控制方面优于文本到3D方法。然而，大多数现有模型在提供快速生成速度和对输入图像高保真度两方面仍存在不足，这两个特征对于实际应用至关重要。本文介绍了一种创新方法 One-2-3-45++，能够将单个图像转换为约一分钟内的详细3D纹理网格。我们的方法旨在充分利用嵌入在2D扩散模型和有限但宝贵的3D数据先验中的广泛知识。首先通过对2D扩散模型进行微调以实现一致的多视角图像生成，然后借助多视角条件下的3D本地扩散模型将这些图像提升到3D。广泛的实验评估表明，我们的方法能够生成高质量、多样化的3D资产，与原始输入图像密切匹配。我们的项目网页：https://sudo-ai-3d.github.io/One2345plus_page。

English

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.

One-2-3-45++：快速单图像到3D对象的生成，具有一致的多视角生成和3D扩散

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

摘要

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