GaussianAnything: Interactive Point Cloud Latent Diffusion for 3D Generation (2411.08033v1)

Abstract: 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.

Interactive Point Cloud Latent Diffusion for 3D Generation

The paper introduces a framework named GAUSSIAN ANYTHING, which aims to significantly enhance the quality, flexibility, and efficiency of 3D content generation. This work addresses critical challenges in the current landscape of 3D generative models, specifically input format limitations, latent space design, and output representation issues. By employing a novel 3D Variational Autoencoder (VAE) and introducing a point cloud-structured latent space, this framework supports multi-modal conditional 3D generation, interactive editing, and achieves superior performance over existing methodologies.

The central contribution of this paper lies in its incorporation of various innovations, notably the use of multi-view posed RGB-D-epth-N-ormal renderings as inputs. This strategy ensures comprehensive 3D information is captured, overcoming the limitations of point cloud inputs that fail to encode high-frequency texture details. The authors propose a point cloud-structured latent space that enables efficient geometry-texture disentanglement, allowing for superior 3D editing capabilities.

The experimental results presented validate the efficacy of the proposed framework across multiple datasets, showcasing its ability to outperform existing methods both in text- and image-conditioned 3D generation tasks. Noteworthy numerical findings indicate significant improvements in 3D fidelity, evidenced by lower Point Cloud FID and KID scores, and superior Coverage and Minimum Matching Distance metrics compared to competing models.

From a theoretical standpoint, GAUSSIAN ANYTHING introduces a significant shift in how latent spaces are structured for 3D diffusion models. By encoding these spaces in a point cloud-structured format, this approach not only facilitates improved editing and generation capabilities but also opens new avenues for the development of more interactive and intuitive 3D editing tools. The implementation of the scene representation transformer architecture for encoding further addresses issues of view consistency and content drift, which are common pitfalls in multi-view 3D generation.

Looking towards the future, the implications of this work suggest potential areas for further investigation, such as the integration of pixel-aligned features to address texture blurriness and the inclusion of real-world conditions to broaden the applicability of the framework. Moreover, exploring additional control mechanisms and expanding upon the dataset variety could significantly enhance the model's utility in practical applications.

In conclusion, GAUSSIAN ANYTHING represents a comprehensive advancement in 3D generative models, providing an innovative solution to existing challenges and setting the groundwork for future research in scalable, interactive, high-quality 3D content generation. This research paves the way for developments in virtual reality, gaming, and other industries reliant on 3D technology, where the demand for flexible, efficient, and high-quality generation methods continues to rise.