UniLat3D: Geometry-Appearance Unified Latents for Single-Stage 3D Generation

• URL: http://arxiv.org/abs/2509.25079v1
• Date: Mon, 29 Sep 2025 17:21:23 GMT
• Title: UniLat3D: Geometry-Appearance Unified Latents for Single-Stage 3D Generation
• Authors: Guanjun Wu, Jiemin Fang, Chen Yang, Sikuang Li, Taoran Yi, Jia Lu, Zanwei Zhou, Jiazhong Cen, Lingxi Xie, Xiaopeng Zhang, Wei Wei, Wenyu Liu, Xinggang Wang, Qi Tian,
• Abstract summary: UniLat3D is a unified framework that encodes geometry and appearance in a single latent space.<n>Our key contribution is a geometry-appearance Unified VAE, which compresses high-resolution sparse features into a compact latent representation.<n>UniLat3D produces high-quality 3D assets in seconds from a single image.
• Score: 98.40254523605581
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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