面向三维生成的原生紧凑结构化潜变量
Native and Compact Structured Latents for 3D Generation
O-Voxel 把几何与 PBR 材质统一编码进稀疏体素，并配一个高压缩率的稀疏压缩 VAE 得到紧凑潜变量，可直接作为 3D 生成模型的表示层替换 NeRF/三平面潜变量；做 3D 生成或资产重建时可借鉴其处理开放、非流形、全封闭拓扑的编码方式与潜空间压缩策略。
原文依据 a new sparse voxel structure called O-Voxel, an omni-voxel representation that encodes both geometry and appearance
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论近期三维生成建模的进展显著提升了生成的真实感，但该领域仍受现有表示方式的制约，这些表示难以刻画具有复杂拓扑与精细外观的资产。本文提出一种从原生三维数据中学习结构化潜变量表示的方法来应对这一挑战。其核心是一种名为 O-Voxel 的新型稀疏体素结构，这是一种同时编码几何与外观的全向体素表示。O-Voxel 能够稳健地建模任意拓扑，包括开放曲面、非流形曲面以及完全封闭的曲面，同时还能捕获纹理颜色之外的全面表面属性，例如基于物理的渲染参数。基于 O-Voxel，我们设计了一个稀疏压缩变分自编码器，它具有较高的空间压缩率和紧凑的潜变量空间。我们利用多样化的公开三维资产数据集，训练了包含 40 亿参数的大规模流匹配模型用于三维生成。尽管规模庞大，其推理仍然十分高效。与此同时，我们生成资产的几何与材质质量远超现有模型。我们相信本方法为三维生成建模带来了显著进展。
英文原文摘要 1222 字符
Recent advancements in 3D generative modeling have significantly improved the generation realism, yet the field is still hampered by existing representations, which struggle to capture assets with complex topologies and detailed appearance. This paper present an approach for learning a structured latent representation from native 3D data to address this challenge. At its core is a new sparse voxel structure called O-Voxel, an omni-voxel representation that encodes both geometry and appearance. O-Voxel can robustly model arbitrary topology, including open, non-manifold, and fully-enclosed surfaces, while capturing comprehensive surface attributes beyond texture color, such as physically-based rendering parameters. Based on O-Voxel, we design a Sparse Compression VAE which provides a high spatial compression rate and a compact latent space. We train large-scale flow-matching models comprising 4B parameters for 3D generation using diverse public 3D asset datasets. Despite their scale, inference remains highly efficient. Meanwhile, the geometry and material quality of our generated assets far exceed those of existing models. We believe our approach offers a significant advancement in 3D generative modeling.