GaussianAnything: Interactive Point Cloud Latent Diffusion for 3D Generation

ICLR 2025 Conference Submission371 Authors

Keywords: 3D Generative Models, Gaussian Splatting, Latent Diffusion Models

TL;DR: A novel interactive 3D generation framework for 3D Gaussians.

Abstract: Recent advancements in diffusion models and large-scale datasets have revolutionized image and video generation, with increasing focus on 3D content generation. While existing methods show promise, they face challenges in input formats, latent space structures, and output representations. This paper introduces a novel 3D generation framework that addresses these issues, enabling scalable and high-quality 3D generation with an interactive Point Cloud-structured Latent space. Our approach utilizes a VAE with multi-view posed RGB-D-N renderings as input, features 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-view image inputs. Experimental results demonstrate superior performance on various datasets, advancing the state-of-the-art in 3D content generation.

Primary Area: generative models

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Submission Number: 371