从一张图生成高保真3D家具模型，支持材质分离与编辑。
Home3D 1.0: A High-Fidelity Image-to-3D Asset Generation System for Interior Design
- 分四模块：几何重建、纹理预测、材质匹配、部件分割，端到端生成
- 输出带PBR材质的封闭网格，可分解为独立材质组件
- 适合室内设计、电商场景，支持语义部件可编辑
我们提出Home3D 1.0，一个面向室内设计与电商应用的模块化图像到3D生成系统。给定一张家具或装饰品的照片，系统输出带有物理渲染（PBR）材质的封闭网格，且网格可分解为特定材质的组件。该流程包含四个紧密耦合模块：Geometry通过潜在SDF建模与粗到细的流匹配DiT重构水密网格；Texture预测多视角反照率并投影至网格，用3D纹理场补全未见区域；Material利用MatWeaver通过视频分割与UV空间投票获取组件掩码，再通过分层多模态匹配从精选材质库中检索并烘焙PBR贴图；Parts使用PartVAE与PartDiT，一次性解码多头部件特异SDF场，生成可编辑语义部件网格。每个模块均采用专用指标独立评估，揭示系统当前能力与迈向广泛应用的现存差距。
原文摘要 · Abstract (English)
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.
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