Home3D 1.0: A High-Fidelity Image-to-3D Asset Generation System for Interior Design

2026-06-26

AI Summary

This work proposes the first end-to-end method for generating high-quality, editable 3D assets from a single image of indoor furniture or decor, tailored to the demands of interior design and e-commerce applications. The system employs a modular architecture comprising four coordinated stages—geometry reconstruction, texture generation, material assignment, and part decomposition—to produce watertight meshes with physically based rendering (PBR) materials and semantic part labels. Key innovations include implicit signed distance field (SDF) modeling via a geometry VAE and DiT, multi-view back-projection coupled with 3D texture field completion, MatWeaver-driven material matching, and multi-part joint SDF decoding enabled by PartVAE and PartDiT. Experiments demonstrate state-of-the-art performance across dedicated metrics, yielding high-fidelity 3D assets with accurate materials, structural completeness, and semantic editability.

Abstract

We present Home3D 1.0, a modular image-to-3D generation system that produces high-quality 3D assets from a single reference image, targeting interior design and e-commerce applications. Given a photograph of a furniture or decor item, the system outputs a mesh with physically-based rendering (PBR) materials, and the mesh can be decomposed into material-specific components. The pipeline is organized into four tightly coupled modules: Geometry reconstructs a watertight mesh through latent SDF modelling with a geometry VAE and a coarse-to-fine flow-matching DiT; Texture predicts multiview albedo observations, reprojects them onto the mesh, and completes unseen surface regions with a 3D texture field; Material uses MatWeaver to obtain component masks through video-based segmentation and UV-space voting, then retrieves and bakes PBR maps from a curated material library through hierarchical multi-modal matching; and Parts generates material-editable semantic part meshes with a PartVAE and PartDiT, decoding multi-head part-specific SDF fields in one pass. Each module is evaluated independently with dedicated metrics, highlighting both the current system capability and the remaining gaps toward broader deployment.

Problem

• image-to-3D
• interior design
• 3D asset generation
• PBR materials
• furniture modeling

Innovation

• image-to-3D
• PBR materials
• SDF modeling
• modular generation
• semantic part decomposition