We propose that the distribution of 3d assets can be modeled as a joint distribution of its corresponding 2d multi-view normal maps and corresponding images.

In this work, we introduce Wonder3D, a novel method for efficiently generating high-fidelity textured meshes from single-view images.

To extract explicit 3D geometry from 2D normal maps and color images, we optimize a neural implicit signed distance field to amalgamate all 2D generated data.

In this paper, we present Wonder3D, an innovative approach designed for efficiently generating high10 fidelity textured meshes from single-view images.