While 3D content generation has advanced significantly, existing methods still face challenges with input formats, latent space design, and output representations. This paper introduces a novel 3D generation framework that addresses these challenges, offering scalable, high-quality 3D generation with an interactive Point Cloud-structured Latent space. Our framework employs a Variational Autoencoder (VAE) with multi-view posed RGB-D(epth)-N(ormal) renderings as input, using a unique latent space design that preserves 3D shape information, and incorporates a cascaded latent diffusion model for improved shape-texture disentanglement. The proposed method, GaussianAnything, supports multi-modal conditional 3D generation, allowing for point cloud, caption, and single/multi-view image inputs. Notably, the newly proposed latent space naturally enables geometry-texture disentanglement, thus allowing 3D-aware editing. Experimental results demonstrate the effectiveness of our approach on multiple datasets, outperforming existing methods in both text- and image-conditioned 3D generation.

本研究解决了3D内容生成中输入格式、潜在空间设计和输出表示的挑战，提出了一种新的3D生成框架。该框架利用变分自编码器（VAE）和独特的潜在空间设计，实现了可扩展的高质量3D生成，并支持多模态条件生成，实验结果表明该方法在多个数据集上超越了现有的方法。

高斯一切：用于3D生成的交互式点云潜在扩散