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

TL;DR

This paper introduces a fast, feed-forward method for converting a single image into a detailed 3D textured mesh in 45 seconds, avoiding lengthy optimization and improving geometry and consistency.

Contribution

It presents a novel approach combining view-conditioned diffusion and SDF-based reconstruction for rapid, high-quality 3D mesh generation from a single image without per-shape optimization.

Findings

Reconstructs 3D meshes in under a minute, significantly faster than existing methods.

Produces more accurate and consistent 3D geometries.

Effective on both synthetic and real-world images.

Abstract

Single image 3D reconstruction is an important but challenging task that requires extensive knowledge of our natural world. Many existing methods solve this problem by optimizing a neural radiance field under the guidance of 2D diffusion models but suffer from lengthy optimization time, 3D inconsistency results, and poor geometry. In this work, we propose a novel method that takes a single image of any object as input and generates a full 360-degree 3D textured mesh in a single feed-forward pass. Given a single image, we first use a view-conditioned 2D diffusion model, Zero123, to generate multi-view images for the input view, and then aim to lift them up to 3D space. Since traditional reconstruction methods struggle with inconsistent multi-view predictions, we build our 3D reconstruction module upon an SDF-based generalizable neural surface reconstruction method and propose several critical training strategies to enable the reconstruction of 360-degree meshes. Without costly optimizations, our method reconstructs 3D shapes in significantly less time than existing methods. Moreover, our method favors better geometry, generates more 3D consistent results, and adheres more closely to the input image. We evaluate our approach on both synthetic data and in-the-wild images and demonstrate its superiority in terms of both mesh quality and runtime. In addition, our approach can seamlessly support the text-to-3D task by integrating with off-the-shelf text-to-image diffusion models.