统一几何与外观的单阶段3D生成，秒级出图且无错位。
UniLat3D: Geometry-Appearance Unified Latents for Single-Stage 3D Generation
- 将几何与外观编码至同一潜在空间，实现单阶段生成。
- 仅用公开数据训练，单图生成3D资产速度达秒级，质量更优。
- 支持3D高斯、网格等多种格式，适合实时3D内容创作。
高质量3D资产生成对多个行业至关重要。尽管近期3D预训练模型在生成真实内容方面表现强劲，但多数基于扩散模型，采用先生成几何再合成外观的两阶段流程，易导致几何与纹理错位且开销较大。本文提出UniLat3D，一个将几何与外观统一编码于单一潜在空间的框架，实现直接单阶段生成。核心贡献为几何-外观统一变分自编码器（VAE），可将高分辨率稀疏特征压缩为紧凑潜在表示——UniLat。UniLat将结构与视觉信息融合为密集低分辨率潜在表示，可高效解码为多种3D格式，如3D高斯和网格。基于此统一表示，我们训练单一流匹配模型，直接将高斯噪声映射至UniLat，消除冗余阶段。仅在公开数据集上训练，UniLat3D可从单张图像秒级生成高质量3D资产，显著提升外观保真度与几何质量。更多演示与代码见https://unilat3d.github.io/
原文摘要 · Abstract (English)
High-fidelity 3D asset generation is crucial for various industries. While recent 3D pretrained models show strong capability in producing realistic content, most are built upon diffusion models and follow a two-stage pipeline that first generates geometry and then synthesizes appearance. Such a decoupled design tends to produce geometry-texture misalignment and non-negligible cost. In this paper, we propose UniLat3D, a unified framework that encodes geometry and appearance in a single latent space, enabling direct single-stage generation. Our key contribution is a geometry-appearance Unified VAE, which compresses high-resolution sparse features into a compact latent representation -- UniLat. UniLat integrates structural and visual information into a dense low-resolution latent, which can be efficiently decoded into diverse 3D formats, e.g., 3D Gaussians and meshes. Based on this unified representation, we train a single flow-matching model to map Gaussian noise directly into UniLat, eliminating redundant stages. Trained solely on public datasets, UniLat3D produces high-quality 3D assets in seconds from a single image, achieving superior appearance fidelity and geometric quality. More demos \& code are available at https://unilat3d.github.io/
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