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Single image 3D reconstruction is an important but challenging task thatrequires extensive knowledge of our natural world. Many existing methods solvethis problem by optimizing a neural radiance field under the guidance of 2Ddiffusion models but suffer from lengthy optimization time, 3D inconsistencyresults, and poor geometry. In this work, we propose a novel method that takesa single image of any object as input and generates a full 360-degree 3Dtextured mesh in a single feed-forward pass. Given a single image, we first usea view-conditioned 2D diffusion model, Zero123, to generate multi-view imagesfor the input view, and then aim to lift them up to 3D space. Since traditionalreconstruction methods struggle with inconsistent multi-view predictions, webuild our 3D reconstruction module upon an SDF-based generalizable neuralsurface reconstruction method and propose several critical training strategiesto enable the reconstruction of 360-degree meshes. Without costlyoptimizations, our method reconstructs 3D shapes in significantly less timethan existing methods. Moreover, our method favors better geometry, generatesmore 3D consistent results, and adheres more closely to the input image. Weevaluate our approach on both synthetic data and in-the-wild images anddemonstrate its superiority in terms of both mesh quality and runtime. Inaddition, our approach can seamlessly support the text-to-3D task byintegrating with off-the-shelf text-to-image diffusion models.

One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization